You searched for subject:(Linear regression).
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University of Bridgeport
1.
Al Essa, Ali.
Efficient Text Classification with Linear Regression Using a Combination of Predictors for Flu Outbreak Detection
.
Degree: 2018, University of Bridgeport
URL: https://scholarworks.bridgeport.edu/xmlui/handle/123456789/3967 
► Early prediction of disease outbreaks and seasonal epidemics such as Influenza may reduce their impact on daily lives. Today, the web can be used for…
(more)
▼ Early prediction of disease outbreaks and seasonal epidemics such as Influenza may reduce their impact on daily lives. Today, the web can be used for surveillance of diseases.Search engines and Social Networking Sites can be used to track trends of different diseases more quickly than government agencies such as Center of Disease Control and Prevention(CDC). Today, Social Networking Sites (SNS) are widely used by diverse demographic populations. Thus, SNS data can be used effectively to track disease outbreaks and provide necessary warnings. Although the generated data of microblogging sites is valuable for real time analysis and outbreak predictions, the volume is huge. Therefore, one of the main challenges in analyzing this huge volume of data is to find the best approach for accurate analysis in an efficient time. Regardless of the analysis time, many studies show only the accuracy of applying different machine learning approaches. Current SNS-based flu detection and prediction frameworks apply conventional machine learning approaches that require lengthy training and testing, which is not the optimal solution for new outbreaks with new signs and symptoms. The aim of this study is to propose an efficient and accurate framework that uses SNS data to track disease outbreaks and provide early warnings, even for newest outbreaks accurately. The presented framework of outbreak prediction consists of three main modules: text classification, mapping, and linear regression for weekly flu rate predictions. The text classification module utilizes the features of sentiment analysis and predefined keyword occurrences. Various classifiers, including FastText and six conventional machine learning algorithms, are evaluated to identify the most efficient and accurate one for the proposed framework. The text classifiers have been trained and tested using a pre-labeled dataset of flu-related and unrelated Twitter postings. The selected text classifier is then used to classify over 8,400,000 tweet documents. The flu-related documents are then mapped ona weekly basis using a mapping module. Lastly, the mapped results are passed together with historical Center for Disease Control and Prevention (CDC) data to a linear regression module for weekly flu rate predictions. The evaluation of flu tweet classification shows that FastText together with the extracted features, has achieved accurate results with anF-measure value of 89.9% in addition to its efficiency. Therefore, FastText has been chosen to be the classification module to work together with the other modules in the proposed framework, including the linear regression module, for flu trend predictions. The prediction results are compared with the available recent data from CDC as the ground truth and show a strong correlation of 96.2%.
Subjects/Keywords: Influenza;
Linear regression
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APA (6th Edition):
Al Essa, A. (2018). Efficient Text Classification with Linear Regression Using a Combination of Predictors for Flu Outbreak Detection
. (Thesis). University of Bridgeport. Retrieved from https://scholarworks.bridgeport.edu/xmlui/handle/123456789/3967
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Al Essa, Ali. “Efficient Text Classification with Linear Regression Using a Combination of Predictors for Flu Outbreak Detection
.” 2018. Thesis, University of Bridgeport. Accessed October 24, 2019.
https://scholarworks.bridgeport.edu/xmlui/handle/123456789/3967.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Al Essa, Ali. “Efficient Text Classification with Linear Regression Using a Combination of Predictors for Flu Outbreak Detection
.” 2018. Web. 24 Oct 2019.
Vancouver:
Al Essa A. Efficient Text Classification with Linear Regression Using a Combination of Predictors for Flu Outbreak Detection
. [Internet] [Thesis]. University of Bridgeport; 2018. [cited 2019 Oct 24].
Available from: https://scholarworks.bridgeport.edu/xmlui/handle/123456789/3967.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Al Essa A. Efficient Text Classification with Linear Regression Using a Combination of Predictors for Flu Outbreak Detection
. [Thesis]. University of Bridgeport; 2018. Available from: https://scholarworks.bridgeport.edu/xmlui/handle/123456789/3967
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Penn State University
2.
Feng, Yijia.
Robust Nonparametric Function Estimation with Serially
Correlated Data.
Degree: PhD, Statistics, 2010, Penn State University
URL: https://etda.libraries.psu.edu/catalog/11391
► Nonparametric function estimation via local polynomial regression has been widely studied in the literature, especially in the past two decades. In practice, we confront two…
(more)
▼ Nonparametric function estimation via local polynomial
regression has been widely studied in the literature, especially in
the past two decades. In practice, we confront two challenges.
Firstly, the local least squares estimator may not be the best
choice when errors are heavily tailed. Secondly, the data are
correlated. In this thesis, we propose robust local linear
smoothing procedures to cope with nonparametric regression and
varying coefficient models with the random error following a
possible heavily tailed autoregressive (AR) process. Unlike
classical local linear technique, our method takes into account the
specific error structure. This correlation information allows us to
improve the estimation efficiency. Furthermore, by applying robust
regression techniques, the new method is insensitive to outliers
comparing with corresponding least squares approaches. Extensive
Monte Carlo simulation studies are conducted to examine the finite
sample performance. From our simulation studies, we find that the
newly proposed procedures dramatically improve the accuracy over
classical procedures under working-independence.
Subjects/Keywords: autoregressive process; local linear regression; robust
regression
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APA (6th Edition):
Feng, Y. (2010). Robust Nonparametric Function Estimation with Serially
Correlated Data. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/11391
Chicago Manual of Style (16th Edition):
Feng, Yijia. “Robust Nonparametric Function Estimation with Serially
Correlated Data.” 2010. Doctoral Dissertation, Penn State University. Accessed October 24, 2019.
https://etda.libraries.psu.edu/catalog/11391.
MLA Handbook (7th Edition):
Feng, Yijia. “Robust Nonparametric Function Estimation with Serially
Correlated Data.” 2010. Web. 24 Oct 2019.
Vancouver:
Feng Y. Robust Nonparametric Function Estimation with Serially
Correlated Data. [Internet] [Doctoral dissertation]. Penn State University; 2010. [cited 2019 Oct 24].
Available from: https://etda.libraries.psu.edu/catalog/11391.
Council of Science Editors:
Feng Y. Robust Nonparametric Function Estimation with Serially
Correlated Data. [Doctoral Dissertation]. Penn State University; 2010. Available from: https://etda.libraries.psu.edu/catalog/11391
3.
Bai, Xue.
Robust linear
regression.
Degree: MS, Department of
Statistics, 2012, Kansas State University
URL: http://hdl.handle.net/2097/14977
► In practice, when applying a statistical method it often occurs that some observations deviate from the usual model assumptions. Least-squares (LS) estimators are very sensitive…
(more)
▼ In practice, when applying a statistical method it
often occurs that some observations deviate from the usual model
assumptions. Least-squares (LS) estimators are very sensitive to
outliers. Even one single atypical value may have a large effect on
the
regression parameter estimates. The goal of robust
regression
is to develop methods that are resistant to the possibility that
one or several unknown outliers may occur anywhere in the data. In
this paper, we review various robust
regression methods including:
M-estimate, LMS estimate, LTS estimate, S-estimate, [tau]-estimate,
MM-estimate, GM-estimate, and REWLS estimate. Finally, we compare
these robust estimates based on their robustness and efficiency
through a simulation study. A real data set application is also
provided to compare the robust estimates with traditional least
squares estimator.
Advisors/Committee Members: Weixin Yao.
Subjects/Keywords: Linear
regression model; Robust
regression; Statistics (0463)
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APA (6th Edition):
Bai, X. (2012). Robust linear
regression. (Masters Thesis). Kansas State University. Retrieved from http://hdl.handle.net/2097/14977
Chicago Manual of Style (16th Edition):
Bai, Xue. “Robust linear
regression.” 2012. Masters Thesis, Kansas State University. Accessed October 24, 2019.
http://hdl.handle.net/2097/14977.
MLA Handbook (7th Edition):
Bai, Xue. “Robust linear
regression.” 2012. Web. 24 Oct 2019.
Vancouver:
Bai X. Robust linear
regression. [Internet] [Masters thesis]. Kansas State University; 2012. [cited 2019 Oct 24].
Available from: http://hdl.handle.net/2097/14977.
Council of Science Editors:
Bai X. Robust linear
regression. [Masters Thesis]. Kansas State University; 2012. Available from: http://hdl.handle.net/2097/14977
Halmstad University
4.
Arslan, Recep Sinan Jr.
Design of a Software Application for Visualization of GPS and Vehicle Data.
Degree: Computer and Electrical Engineering (IDE), 2009, Halmstad University
URL: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-4140
► I present an application to visualization of GPS data and Linear Correlations and models. A collection of data for each vehicle is used to…
(more)
▼ I present an application to visualization of GPS data and Linear Correlations and models. A collection of data for each vehicle is used to compute correlations. Deviating correlations can be indicative of a faulty vehicle. The correlation values for each vehicle are computed with use linear regression algorithms using up to 4 signals in the data (with varied time window), and display the model parameters in a window next to the GPS map. Multiple measurements (multiple drive routes and multiple model parameters) are displayed at the same time, allowing tracking over time and comparison of different vehicles. The whole technique is demonstrated on three data which is set on first frame by user. The results are displayed with a java application and Google Map.
Subjects/Keywords: Deviation Detection; Linear Regression; GPS
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APA (6th Edition):
Arslan, R. S. J. (2009). Design of a Software Application for Visualization of GPS and Vehicle Data. (Thesis). Halmstad University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-4140
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Arslan, Recep Sinan Jr. “Design of a Software Application for Visualization of GPS and Vehicle Data.” 2009. Thesis, Halmstad University. Accessed October 24, 2019.
http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-4140.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Arslan, Recep Sinan Jr. “Design of a Software Application for Visualization of GPS and Vehicle Data.” 2009. Web. 24 Oct 2019.
Vancouver:
Arslan RSJ. Design of a Software Application for Visualization of GPS and Vehicle Data. [Internet] [Thesis]. Halmstad University; 2009. [cited 2019 Oct 24].
Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-4140.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Arslan RSJ. Design of a Software Application for Visualization of GPS and Vehicle Data. [Thesis]. Halmstad University; 2009. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-4140
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Georgia
5.
Ganesan, Sivanesan.
Regression Leaf Forest: a fast and accurate learning method for large & high dimensional data sets.
Degree: MS, Computer Science, 2011, University of Georgia
URL: http://purl.galileo.usg.edu/uga_etd/ganesan_sivanesan_201105_ms
► There are a number of learning methods that provide solutions to classification and regression problems, including Linear Regression, Decision Trees, KNN, and SVMs. These methods…
(more)
▼ There are a number of learning methods that provide solutions to classification and
regression problems, including
Linear Regression, Decision Trees, KNN, and SVMs. These methods work well in many applications, but they are challenged for real world problems that are noisy, non-
linear or high dimensional. Furthermore, missing data (e.g., missing historical features of companies in stock data), is not managed well by current approaches. We present an implementation of a hybrid learning system that combines an ensemble of decision trees (Random Forest) with of
Linear Regression.
Linear Regression (LR) is fast but not accurate because it assumes linearity, while Random Forests are not as fast as LR but have been shown to be accurate for high dimensional and large data sets. By combining these approaches we address the weaknesses of each approach and exploit their strengths both in terms of real time performance and accuracy.
In this thesis, we evaluate a hybrid Random Forest and
Linear Regression implementation called "
Regression Leaf Forest", which is a forest of trees with
regression leaves for supervised learning problems. The approach extends Random Forests by introducing
Linear Regression learners at the leaf nodes of the trees for predicting functions. Our empirical analysis on both real and artificial data shows that the proposed algorithm requires less computation time for both large and high-dimensional datasets while providing comparable or better accuracy when compared to: Single Tree, a Single
Linear Regression Tree, and Random Forest algorithms.
Advisors/Committee Members: Maria Hybinette.
Subjects/Keywords: Random Forest; Linear Regression
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APA (6th Edition):
Ganesan, S. (2011). Regression Leaf Forest: a fast and accurate learning method for large & high dimensional data sets. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/ganesan_sivanesan_201105_ms
Chicago Manual of Style (16th Edition):
Ganesan, Sivanesan. “Regression Leaf Forest: a fast and accurate learning method for large & high dimensional data sets.” 2011. Masters Thesis, University of Georgia. Accessed October 24, 2019.
http://purl.galileo.usg.edu/uga_etd/ganesan_sivanesan_201105_ms.
MLA Handbook (7th Edition):
Ganesan, Sivanesan. “Regression Leaf Forest: a fast and accurate learning method for large & high dimensional data sets.” 2011. Web. 24 Oct 2019.
Vancouver:
Ganesan S. Regression Leaf Forest: a fast and accurate learning method for large & high dimensional data sets. [Internet] [Masters thesis]. University of Georgia; 2011. [cited 2019 Oct 24].
Available from: http://purl.galileo.usg.edu/uga_etd/ganesan_sivanesan_201105_ms.
Council of Science Editors:
Ganesan S. Regression Leaf Forest: a fast and accurate learning method for large & high dimensional data sets. [Masters Thesis]. University of Georgia; 2011. Available from: http://purl.galileo.usg.edu/uga_etd/ganesan_sivanesan_201105_ms
6.
AJMAL, KHAN.
An Analysis of the Telecommunications Business in China by Linear Regression.
Degree: Statistics, 2010, Dalarna University
URL: http://urn.kb.se/resolve?urn=urn:nbn:se:du-4826
► In this paper, we study the influence of the National Telecom Business Volume by the data in 2008 that have been published in China…
(more)
▼ In this paper, we study the influence of the National Telecom Business Volume by the data in 2008 that have been published in China Statistical Yearbook of Statistics. We illustrate the procedure of modeling “National Telecom Business Volume” on the following eight variables, GDP, Consumption Levels, Retail Sales of Social Consumer Goods Total Renovation Investment, the Local Telephone Exchange Capacity, Mobile Telephone Exchange Capacity, Mobile Phone End Users, and the Local Telephone End Users. The testing of heteroscedasticity and multicollinearity for model evaluation is included. We also consider AIC and BIC criterion to select independent variables, and conclude the result of the factors which are the optimal regression model for the amount of telecommunications business and the relation between independent variables and dependent variable. Based on the final results, we propose several recommendations about how to improve telecommunication services and promote the economic development.
Subjects/Keywords: Linear regression; telecommunication; modeling
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APA (6th Edition):
AJMAL, K. (2010). An Analysis of the Telecommunications Business in China by Linear Regression. (Thesis). Dalarna University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:du-4826
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
AJMAL, KHAN. “An Analysis of the Telecommunications Business in China by Linear Regression.” 2010. Thesis, Dalarna University. Accessed October 24, 2019.
http://urn.kb.se/resolve?urn=urn:nbn:se:du-4826.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
AJMAL, KHAN. “An Analysis of the Telecommunications Business in China by Linear Regression.” 2010. Web. 24 Oct 2019.
Vancouver:
AJMAL K. An Analysis of the Telecommunications Business in China by Linear Regression. [Internet] [Thesis]. Dalarna University; 2010. [cited 2019 Oct 24].
Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:du-4826.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
AJMAL K. An Analysis of the Telecommunications Business in China by Linear Regression. [Thesis]. Dalarna University; 2010. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:du-4826
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Youngstown State University
7.
Jamison, Jonathan A.
Linear Regression Analysis of the Suspended Sediment Load in
Rivers and Streams Using Data of Similar Precipitation
Values.
Degree: MSin Environmental Science, Department of Geological & Environmental
Sciences, 2018, Youngstown State University
URL: http://rave.ohiolink.edu/etdc/view?acc_num=ysu154273822580279
► Sediment provides a method for transportation of a variety of other pollutants such as nutrients and potentially harmful bacteria. In addition, sediment can increase the…
(more)
▼ Sediment provides a method for transportation of a
variety of other pollutants such as nutrients and potentially
harmful bacteria. In addition, sediment can increase the cost of
water treatment processes and reduce storage volume of water
reservoirs. This study employs
linear regression to predict the
annual suspended sediment load, a dependent variable, as a function
of the annual river water discharge, an independent variable in
four United States Rivers. The available data (annual suspended
sediment load and annual river water discharge) for each river was
broken down into groups based upon similar precipitation values.
Each river was divided into two or three groups, with a total of
ten groups for the four rivers.
Linear regression was applied to
each group. Results of the precipitation approach were compared to
those of the traditional approach, the latter did not use any
precipitation data and thus there is no individual groupings. The
precipitation approach provided higher accuracy for the prediction
of the suspended sediment load when compared to the traditional
approach. The prediction accuracy is evident from the high
correlation coefficient values (between the suspended sediment and
river water discharge), and the low percent deviations (percent
difference between the observed and predicted suspended sediment).
Of the ten river groups, seven resulted in higher correlation
coefficients, and five gave lower percent deviations compared to
the traditional approach. The mean percent deviation ranged between
20 and 26% in seven groups, which is considered an indication of
high accuracy when suspended sediment is predicted by
linear
regression. All of the ten groups resulted in higher correlation
coefficient values greater or equal to 0.80, with four groups
exceeding 0.90.
Advisors/Committee Members: Amin, Isam (Committee Chair).
Subjects/Keywords: Environmental Science; Geomorphology; linear regression
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APA ·
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Manager
APA (6th Edition):
Jamison, J. A. (2018). Linear Regression Analysis of the Suspended Sediment Load in
Rivers and Streams Using Data of Similar Precipitation
Values. (Masters Thesis). Youngstown State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ysu154273822580279
Chicago Manual of Style (16th Edition):
Jamison, Jonathan A. “Linear Regression Analysis of the Suspended Sediment Load in
Rivers and Streams Using Data of Similar Precipitation
Values.” 2018. Masters Thesis, Youngstown State University. Accessed October 24, 2019.
http://rave.ohiolink.edu/etdc/view?acc_num=ysu154273822580279.
MLA Handbook (7th Edition):
Jamison, Jonathan A. “Linear Regression Analysis of the Suspended Sediment Load in
Rivers and Streams Using Data of Similar Precipitation
Values.” 2018. Web. 24 Oct 2019.
Vancouver:
Jamison JA. Linear Regression Analysis of the Suspended Sediment Load in
Rivers and Streams Using Data of Similar Precipitation
Values. [Internet] [Masters thesis]. Youngstown State University; 2018. [cited 2019 Oct 24].
Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ysu154273822580279.
Council of Science Editors:
Jamison JA. Linear Regression Analysis of the Suspended Sediment Load in
Rivers and Streams Using Data of Similar Precipitation
Values. [Masters Thesis]. Youngstown State University; 2018. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ysu154273822580279
University of Arizona
8.
Clutter, Melissa Jean.
Designing Robust Measurement Networks Using Universal Multiple Linear Regression
.
Degree: 2019, University of Arizona
URL: http://hdl.handle.net/10150/633093
► Collecting hydrological data is essential for understanding system behavior and processes; without it, there is no basis for predictive modeling or risk assessment. Unfortunately, limited…
(more)
▼ Collecting hydrological data is essential for understanding system behavior and processes; without it, there is no basis for predictive modeling or risk assessment. Unfortunately, limited monitoring budgets often restrict measurement designs for field-based studies. Therefore, most field studies require some sort of data worth analysis to identify the most important data to collect with respect to the prediction(s) of interest. Data worth analyses can be either informal using methods such as trial-and-error, intuition, or rules of thumb, or formal using a quantitative metric to identify the most valuable data. My research focuses on a simple, computationally inexpensive formal data worth analysis which can be used in conjunction with more complex optimization approaches or when they are not warranted.
A key to network design is that the selection of sensor type, timing, and placement should be both informative and efficient. There are many possible individual sensor types and installation depths, and the key is to determine which sets of observations would be most effective prior to data collection. My research explores a combination of a method called universal Multiple
Linear Regression (uMLR) and Robust Decision Making (RDM) to identify these best observation sets. The uMLR method quantifies the explanatory power of all possible combinations of observations to the prediction(s) of interest and the RDM strategy further explores the impacts of user-defined uncertainties, including measurement error and parameter uncertainty, on these observation-set selections. Robust Decision Making is a concept developed by the Research and Development (RAND) Corporation and is designed to select a robust outcome under a range of uncertainty, at the risk of the selection being sub-optimal for any one specific uncertain outcome. Norgaard et al., (2014) previously used the uMLR approach to downsampling pre-existing data to identify a reduced set of parameters to describe the dispersibility of colloids. I offer an extension of the uMLR downsampling approach, based on model-simulated data, to consider optimizing data that have not yet been collected.
Advisors/Committee Members: Ferre, Ty (advisor), Meixner, Thomas (committeemember), Schaap, Marcel (committeemember).
Subjects/Keywords: design;
linear regression;
measurement;
optimization
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APA ·
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Export
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Manager
APA (6th Edition):
Clutter, M. J. (2019). Designing Robust Measurement Networks Using Universal Multiple Linear Regression
. (Doctoral Dissertation). University of Arizona. Retrieved from http://hdl.handle.net/10150/633093
Chicago Manual of Style (16th Edition):
Clutter, Melissa Jean. “Designing Robust Measurement Networks Using Universal Multiple Linear Regression
.” 2019. Doctoral Dissertation, University of Arizona. Accessed October 24, 2019.
http://hdl.handle.net/10150/633093.
MLA Handbook (7th Edition):
Clutter, Melissa Jean. “Designing Robust Measurement Networks Using Universal Multiple Linear Regression
.” 2019. Web. 24 Oct 2019.
Vancouver:
Clutter MJ. Designing Robust Measurement Networks Using Universal Multiple Linear Regression
. [Internet] [Doctoral dissertation]. University of Arizona; 2019. [cited 2019 Oct 24].
Available from: http://hdl.handle.net/10150/633093.
Council of Science Editors:
Clutter MJ. Designing Robust Measurement Networks Using Universal Multiple Linear Regression
. [Doctoral Dissertation]. University of Arizona; 2019. Available from: http://hdl.handle.net/10150/633093
9.
Peraça, Maria da Graça Teixeira.
Modelos para estimativa do grau de saturação do concreto mediante variáveis ambientais que influenciam na sua variação.
Degree: 2009, Universidade Federal do Rio Grande
URL: http://repositorio.furg.br/handle/1/3436
► Dissertação(mestrado) - Universidade Federal do Rio Grande, Programa de Pós-Graduação em Engenharia Oceânica, Escola de Engenharia, 2009.
Nas engenharias, é fundamental estimar o tempo de…
(more)
▼ Dissertação(mestrado) - Universidade Federal do Rio Grande, Programa de Pós-Graduação em Engenharia Oceânica, Escola de Engenharia, 2009.
Nas engenharias, é fundamental estimar o tempo de vida útil das estruturas construídas, o que neste trabalho significa o tempo que os íons cloretos levam para atingirem a armadura do concreto. Um dos coeficientes que influenciam na vida útil do concreto é o de difusão, sendo este diretamente influenciado pelo grau de saturação (GS) do concreto. Recentes estudos
levaram ao desenvolvimento de um método de medição do GS. Embora esse método seja
eficiente, ainda assim há um grande desperdício de tempo e dinheiro em utilizá-lo. O objetivo deste trabalho é reduzir estes custos calculando uma boa aproximação para o valor do GS com modelos matemáticos que estimem o seu valor através de variáveis ambientais que influenciam na sua variação. As variáveis analisadas nesta pesquisa, são: pressão atmosférica,temperatura do ar seco, temperatura máxima, temperatura mínima, taxa de evaporação interna (Pichê), taxa de precipitação, umidade relativa, insolação, visibilidade, nebulosidade e taxa de
evaporação externa. Todas foram analisadas e comparadas estatisticamente com medidas do
GS obtidas durante quatro anos de medições semanais, para diferentes famílias de concreto. Com essas análises, pode-se medir a relação entre estes dados verificando que os fatores mais influentes no GS são, temperatura máxima e umidade relativa. Após a verificação desse resultado, foram elaborados modelos estatísticos, para que, através dos dados ambientais, cedidos pelo banco de dados meteorológicos, se possam calcular, sem desperdício de tempo e dinheiro, as médias aproximadas do GS para cada estação sazonal da região sul do Brasil, garantindo assim uma melhor estimativa do tempo de vida útil em estruturas de concreto.
In engineering, it is fundamental to estimate the life-cycle of built structures, which in this study means the period of time required for chlorides to reach the concrete reinforcement. One of the coefficients that affect the life-cycle of concrete is the diffusion, which is directly influenced by the saturation degree (SD) of concrete. Recent studies have led to the development of a measurement method for the SD. Although this method is efficient, there is still waste of time and money when it is used. The objective of this study is to reduce costs by
calculating a good approximation for the SD value with mathematical models that predict its value through environmental variables that affect its variation. The variables analysed in the study are: atmospheric pressure, temperature of the dry air, maximum temperature, minimum temperature, internal evaporation rate (Pichê), precipitation rate, relative humidity, insolation, visibility, cloudiness and external evaporation rate. All of them were statistically analysed and compared with measurements of SD obtained during four years of weekly assessments for different families of concrete. By considering these analyses, the relationship among these data…
Advisors/Committee Members: Guimarães, André Tavares da Cunha.
Subjects/Keywords: Temperatura máxima; Regressão linear simples; Regressão linear múltipla; Maximum temperature; Linear regression; Multiple linear regression
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APA (6th Edition):
Peraça, M. d. G. T. (2009). Modelos para estimativa do grau de saturação do concreto mediante variáveis ambientais que influenciam na sua variação. (Masters Thesis). Universidade Federal do Rio Grande. Retrieved from http://repositorio.furg.br/handle/1/3436
Chicago Manual of Style (16th Edition):
Peraça, Maria da Graça Teixeira. “Modelos para estimativa do grau de saturação do concreto mediante variáveis ambientais que influenciam na sua variação.” 2009. Masters Thesis, Universidade Federal do Rio Grande. Accessed October 24, 2019.
http://repositorio.furg.br/handle/1/3436.
MLA Handbook (7th Edition):
Peraça, Maria da Graça Teixeira. “Modelos para estimativa do grau de saturação do concreto mediante variáveis ambientais que influenciam na sua variação.” 2009. Web. 24 Oct 2019.
Vancouver:
Peraça MdGT. Modelos para estimativa do grau de saturação do concreto mediante variáveis ambientais que influenciam na sua variação. [Internet] [Masters thesis]. Universidade Federal do Rio Grande; 2009. [cited 2019 Oct 24].
Available from: http://repositorio.furg.br/handle/1/3436.
Council of Science Editors:
Peraça MdGT. Modelos para estimativa do grau de saturação do concreto mediante variáveis ambientais que influenciam na sua variação. [Masters Thesis]. Universidade Federal do Rio Grande; 2009. Available from: http://repositorio.furg.br/handle/1/3436
Anna University
10.
Arulchinnappan S.
A study on fuzzy linear regression.
Degree: Mathematics, 2012, Anna University
URL: http://shodhganga.inflibnet.ac.in/handle/10603/17758
► The fuzzy set theory was first proposed by Zadeh in 1965. The fuzzy linear regression was proposed by Tanaka et al in 1982. Many different…
(more)
▼ The fuzzy set theory was first proposed by Zadeh in
1965. The fuzzy linear regression was proposed by Tanaka et al in
1982. Many different fuzzy regression approaches have been proposed
by different researchers. Since then this subject has drawn much
attention from more and more people concerned. There are two
approaches in fuzzy regression analysis, one is linear programming
based method and another one is fuzzy least square method. The
first method is based on minimizing fuzziness as an optimal
criterion and the second method is fuzzy least square method, which
is based on the notion of the distance between the predicted fuzzy
outputs and the observed fuzzy outputs and the goodness-of-fit. In
this work, we have used the Tanaka s possibilistic fuzzy linear
regression. We have considered a symmetric triangular fuzzy number
and have incorporated the concept of fuzzy linear system into the
fuzzy linear regression. The fuzzy coefficients involved in the
regression line are determined using normalized equations and the
fuzzy lines are determined. A numerical example is solved to
illustrate the efficiency of the proposed method. Next the fuzzy
linear regression equation is applied for the waste water treatment
to identify the chemical factors. Iv An algorithm for the newly
formulated fuzzy linear regression is developed. Data of oral
cancer patients have been collected and used in the algorithm to
identify the risk factors involved in oral cancer. The classical
correlation is converted into fuzzy correlation model. The fuzzy
lines of y on x and x on y are obtained. The collected oral cancer
data are utilized to identify the risk factors of oral cancer. The
data employed in fuzzy linear regression, algorithm and fuzzy
correlation methods have given reasonably good results in the
prediction of risk factors involved in the oral cancer. These
methods can serve as a powerful mathematical tool to predict the
risk factors involved in the chronic diseases in medical
domain.
References p. 75-89
Advisors/Committee Members: Viswanathan R.
Subjects/Keywords: Chronic diseases; Fuzzy set theory; Linear regression; Fuzzy linear regression
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to Zotero / EndNote / Reference
Manager
APA (6th Edition):
S, A. (2012). A study on fuzzy linear regression. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/17758
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
S, Arulchinnappan. “A study on fuzzy linear regression.” 2012. Thesis, Anna University. Accessed October 24, 2019.
http://shodhganga.inflibnet.ac.in/handle/10603/17758.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
S, Arulchinnappan. “A study on fuzzy linear regression.” 2012. Web. 24 Oct 2019.
Vancouver:
S A. A study on fuzzy linear regression. [Internet] [Thesis]. Anna University; 2012. [cited 2019 Oct 24].
Available from: http://shodhganga.inflibnet.ac.in/handle/10603/17758.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
S A. A study on fuzzy linear regression. [Thesis]. Anna University; 2012. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/17758
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
11.
Pandiri, Venkat Shiva.
Machine Learning Models to Predict House Prices based on Home Features
.
Degree: 2017, California State University – San Marcos
URL: http://hdl.handle.net/10211.3/194683
► This project carried out a systematic investigation to predict the final price of each home using machine learning techniques. Various machine learning techniques such as…
(more)
▼ This project carried out a systematic investigation to predict the final price of each home using machine learning techniques. Various machine learning techniques such as multiple
linear regression (base model), random forest
regression and polynomial
regression were applied to the dataset to compare the results. The data describes the sale of individual properties, various features and details of each home in Ames, IW from 2006 to 2010. The dataset comprises of 80 explanatory variables which include 23 nominal, 23 ordinal, 14 discrete, and 20 continuous variables. The programs were implemented using Python, by using core libraries like pandas, scikit???learn, NumPy. Backward elimination algorithm is applied in building optimal model and selection of features over 270 independent variables with approximately 7,91,320 observations. K-fold cross validation technique is used to measure the performance of all the models. A good high R- squared values with low variance are recorded for
linear models. In order to select a good prediction model, all the
regression models are explored and compared with each other. Results from K fold cross validation indicates high R-squared values for MLR and Random forest, stating a high level of performance when applied on an actual test set. Each model is evaluated with kaggle score checker. My Random forest model achieved the score of 0.14696, which is better compared to my base model Multiple
linear regression (kaggle score 0.16854) and Polynomial
regression (kaggle score 0.24399).
Advisors/Committee Members: Zhang, Xiaoyu (advisor), Ye, Xin (committeemember).
Subjects/Keywords: Multiple Linear Regression;
Random Forest Regression;
Polynomial Regression
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to Zotero / EndNote / Reference
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APA (6th Edition):
Pandiri, V. S. (2017). Machine Learning Models to Predict House Prices based on Home Features
. (Thesis). California State University – San Marcos. Retrieved from http://hdl.handle.net/10211.3/194683
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Pandiri, Venkat Shiva. “Machine Learning Models to Predict House Prices based on Home Features
.” 2017. Thesis, California State University – San Marcos. Accessed October 24, 2019.
http://hdl.handle.net/10211.3/194683.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Pandiri, Venkat Shiva. “Machine Learning Models to Predict House Prices based on Home Features
.” 2017. Web. 24 Oct 2019.
Vancouver:
Pandiri VS. Machine Learning Models to Predict House Prices based on Home Features
. [Internet] [Thesis]. California State University – San Marcos; 2017. [cited 2019 Oct 24].
Available from: http://hdl.handle.net/10211.3/194683.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Pandiri VS. Machine Learning Models to Predict House Prices based on Home Features
. [Thesis]. California State University – San Marcos; 2017. Available from: http://hdl.handle.net/10211.3/194683
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Georgia
12.
Harrell, Leigh Michelle.
Regression models in standardized test prediction.
Degree: MS, Statistics, 2004, University of Georgia
URL: http://purl.galileo.usg.edu/uga_etd/harrell_leigh_m_200408_ms
► The objective of this study is to identify the predictors of graduation exam passing rates on standardized tests administered high schools in the United States…
(more)
▼ The objective of this study is to identify the predictors of graduation exam passing rates on standardized tests administered high schools in the United States using weighted
linear regression models. Data from high schools in California, Illinois, and Texas were analyzed separately to obtain state-wide predictors, as well as together as one large dataset for overall predictors.
Regression models containing only student-related variables, such as the percent of minority students, percent of low- income students, percent of “Limited English Proficient” students, were better predictors than models that contained only school-related variables, such as total enrollment and number of grade levels. When compared to the best overall model for the combined dataset, all of the states used subsets of the model. Within Texas, the predictors for the percentage of Black students passing were different from those for White and Hispanic students.
Advisors/Committee Members: Mary Meyer.
Subjects/Keywords: weighted linear regression
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Chicago ·
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to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Harrell, L. M. (2004). Regression models in standardized test prediction. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/harrell_leigh_m_200408_ms
Chicago Manual of Style (16th Edition):
Harrell, Leigh Michelle. “Regression models in standardized test prediction.” 2004. Masters Thesis, University of Georgia. Accessed October 24, 2019.
http://purl.galileo.usg.edu/uga_etd/harrell_leigh_m_200408_ms.
MLA Handbook (7th Edition):
Harrell, Leigh Michelle. “Regression models in standardized test prediction.” 2004. Web. 24 Oct 2019.
Vancouver:
Harrell LM. Regression models in standardized test prediction. [Internet] [Masters thesis]. University of Georgia; 2004. [cited 2019 Oct 24].
Available from: http://purl.galileo.usg.edu/uga_etd/harrell_leigh_m_200408_ms.
Council of Science Editors:
Harrell LM. Regression models in standardized test prediction. [Masters Thesis]. University of Georgia; 2004. Available from: http://purl.galileo.usg.edu/uga_etd/harrell_leigh_m_200408_ms
University of Vermont
13.
Berger, Alex.
Using Crowdsourcing to Discover Correlational Relationships.
Degree: Computer Science, 2014, University of Vermont
URL: https://scholarworks.uvm.edu/hcoltheses/33
► Obtaining hypotheses for scientific experiments can be an exceptionally challenging and time-consuming task. Even with specialized knowledge, researchers frequently overlook important variables that may…
(more)
▼ Obtaining hypotheses for scientific experiments can be an exceptionally challenging and time-consuming task. Even with specialized knowledge, researchers frequently overlook important variables that may be strongly correlated with their outcome of interest. This experiment explores the use of crowdsourcing as a potentially more efficient way of gathering hypotheses. Testing the effectiveness of crowdsourcing involved the creation of a website that would enable users to build an online survey consisting of questions that may be predictive of a specified response variable. Contributions were obtained from members of online communities interested in subjects related to the area of research being studied. Allowing users to answer preexisting questions and add new questions to the survey resulted in a substantial amount of data on hundreds of potentially related variables. This data could then be used to determine which of the proposed factors are most correlated with the outcome of interest. Strongly correlated variables could later be studied in more detail in future experiments. Though this experiment specifically aims to identify factors that may be predictive of someone’s personal savings, this method of crowdsourcing can be replicated by researchers in any field of study. Using crowdsourcing to gather preliminary data has the potential to improve the efficiency of academic research and increase the rate of scientific discoveries.
Advisors/Committee Members: Josh Bongard.
Subjects/Keywords: crowdsourcing; computer science; machine learning; linear regression
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Manager
APA (6th Edition):
Berger, A. (2014). Using Crowdsourcing to Discover Correlational Relationships. (Thesis). University of Vermont. Retrieved from https://scholarworks.uvm.edu/hcoltheses/33
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Berger, Alex. “Using Crowdsourcing to Discover Correlational Relationships.” 2014. Thesis, University of Vermont. Accessed October 24, 2019.
https://scholarworks.uvm.edu/hcoltheses/33.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Berger, Alex. “Using Crowdsourcing to Discover Correlational Relationships.” 2014. Web. 24 Oct 2019.
Vancouver:
Berger A. Using Crowdsourcing to Discover Correlational Relationships. [Internet] [Thesis]. University of Vermont; 2014. [cited 2019 Oct 24].
Available from: https://scholarworks.uvm.edu/hcoltheses/33.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Berger A. Using Crowdsourcing to Discover Correlational Relationships. [Thesis]. University of Vermont; 2014. Available from: https://scholarworks.uvm.edu/hcoltheses/33
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Baylor University
14.
[No author].
Topics in Bayesian adaptive clinical trial design using dynamic linear models and missing data imputation in logistic regression.
Degree: 2015, Baylor University
URL: http://hdl.handle.net/2104/9557
► Conventional Phase II clinical trial designs usually employ a logistic regression model to analyze the efficacy of a new drug and, therefore, assumes monotone dose-response…
(more)
▼ Conventional Phase II clinical trial designs usually employ a logistic
regression model to analyze the efficacy of a new drug and, therefore, assumes monotone dose-response relationship. Also, the logistic
regression model requires the response to be categorical and, thus, it is not applicable for continuous data. The traditional design in Phase II determines if a new drug will be further tested in Phase III based on only drug efficacy and allocates an equal number of patients to each dosage, ignoring dose efficacy. Because of the limitations of conventional clinical trial designs, new adaptive designs have been proposed by researchers to improve the flexibility and adaptability of conventional designs. In Chapter Two we propose an adaptive Bayesian design that uses a bivariate normal dynamic
linear model for a Phase II clinical trial, and we compare its performance to a Bayesian fixed or non-adaptive design. The proposed Bayesian adaptive design can be utilized for continuous data and can model various dose-response relationships. We remark that for many dose-response relationships, our proposed adaptive Bayesian design can use fewer patients to obtain a correct decision concerning a drug's efficacy than the Bayesian fixed design. Missing data arises in almost all research; that is, part of the data are missing for a
subject. A data analyst must decide how to cope with the missing data from among the numerous imputation methods that can be used. However, one might not know which imputation method is the best. The objective of this study is to evaluate the efficacy of five imputation methods. In Chapter Four, we have compared the performance of complete-data-only, single-mean imputation, conditional-mean imputation, multiple imputation by chained equations and hotdeck imputation methods for prediction of a logistic
regression model, for the missing-completely-at-random and missing-at-random mechanisms. These five imputation methods yield different results for small sample sizes, and the difference decreases with an increasing sample size. Surprisingly, a single-mean imputation method performs as well as the multiple imputation methods compared here.
Advisors/Committee Members: Young, Dean M (advisor).
Subjects/Keywords: Clinical trial. Dynamic linear model. Logistic regression.
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APA (6th Edition):
author], [. (2015). Topics in Bayesian adaptive clinical trial design using dynamic linear models and missing data imputation in logistic regression.
(Thesis). Baylor University. Retrieved from http://hdl.handle.net/2104/9557
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
author], [No. “Topics in Bayesian adaptive clinical trial design using dynamic linear models and missing data imputation in logistic regression.
” 2015. Thesis, Baylor University. Accessed October 24, 2019.
http://hdl.handle.net/2104/9557.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
author], [No. “Topics in Bayesian adaptive clinical trial design using dynamic linear models and missing data imputation in logistic regression.
” 2015. Web. 24 Oct 2019.
Vancouver:
author] [. Topics in Bayesian adaptive clinical trial design using dynamic linear models and missing data imputation in logistic regression.
[Internet] [Thesis]. Baylor University; 2015. [cited 2019 Oct 24].
Available from: http://hdl.handle.net/2104/9557.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
author] [. Topics in Bayesian adaptive clinical trial design using dynamic linear models and missing data imputation in logistic regression.
[Thesis]. Baylor University; 2015. Available from: http://hdl.handle.net/2104/9557
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Penn State University
15.
Jiang, Yuejiao.
Estimation and forecasting methodologies for.
Degree: PhD, Statistics, 2009, Penn State University
URL: https://etda.libraries.psu.edu/catalog/9833
► Nonparametric regression models have been used to explore features of data. Various estimation procedures have been proposed for estimating nonparametric regression models in the literature.…
(more)
▼ Nonparametric regression models have been used to
explore features of data. Various estimation procedures have been
proposed for estimating nonparametric regression models in the
literature. The existing procedures may enjoy some statistically
optimal properties, but it is difficult to use the estimated
regression functions for the purpose of forecasting, i.e.,
prediction with independent variables beyond the observed range.
Motivated by dynamic linear model estimation and forecasting
methodologies developed by citeA{west1997}, our research extends
the study in two directions by building connections between
nonparametric regression models and dynamic linear models. First,
for nonparametric regression models with independently and
identically distributed errors, we develop a regularized regression
spline to determine hyper-parameter values for errors and compare
estimation performance with a local linear estimator, a commonly
used smoothing method for nonparametric regression. Second we study
nonparametric regression models with autoregressive errors process
since when collected over time, data are likely to be serially
correlated. From our simulation studies, the proposed methods
outperform the local linear regression under certain statistical
settings for both models with i.i.d. and autoregressive error.
Last, we establish a connection between varying coefficient models
and dynamic linear models. % what are "certain statistical
settings" ? % with an autoregressive error process. We %provide
some insights into the selection of hyper-parameter in the %varying
coefficient models to save computing time. Both estimation and
forecasting performances of the proposed method are examined via
simulations. The proposed methods perform well for larger sample
sizes. The proposed procedures are demonstrated by analysis of two
real data examples.
Subjects/Keywords: nonparametric regression models; dynamic linear
models
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Manager
APA (6th Edition):
Jiang, Y. (2009). Estimation and forecasting methodologies for. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/9833
Chicago Manual of Style (16th Edition):
Jiang, Yuejiao. “Estimation and forecasting methodologies for.” 2009. Doctoral Dissertation, Penn State University. Accessed October 24, 2019.
https://etda.libraries.psu.edu/catalog/9833.
MLA Handbook (7th Edition):
Jiang, Yuejiao. “Estimation and forecasting methodologies for.” 2009. Web. 24 Oct 2019.
Vancouver:
Jiang Y. Estimation and forecasting methodologies for. [Internet] [Doctoral dissertation]. Penn State University; 2009. [cited 2019 Oct 24].
Available from: https://etda.libraries.psu.edu/catalog/9833.
Council of Science Editors:
Jiang Y. Estimation and forecasting methodologies for. [Doctoral Dissertation]. Penn State University; 2009. Available from: https://etda.libraries.psu.edu/catalog/9833
16.
Mahmood, Arshad.
Rainfall prediction in Australia : Clusterwise linear regression approach.
Degree: PhD, 2017, Federation University Australia
URL: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/159251
;
https://library.federation.edu.au/record=b2722196
► Accurate rainfall prediction is a challenging task because of the complex physical processes involved. This complexity is compounded in Australia as the climate can be…
(more)
▼ Accurate rainfall prediction is a challenging task because of the complex physical processes involved. This complexity is compounded in Australia as the climate can be highly variable. Accurate rainfall prediction is immensely benecial for making informed policy, planning and management decisions, and can assist with the most sustainable operation of water resource systems. Short-term prediction of rainfall is provided by meteorological services; however, the intermediate to long-term prediction of rainfall remains challenging and contains much uncertainty. Many prediction approaches have been proposed in the literature, including statistical and computational intelligence approaches. However, finding a method to model the complex physical process of rainfall, especially in Australia where the climate is highly variable, is still a major challenge. The aims of this study are to: (a) develop an optimization based clusterwise linear regression method, (b) develop new prediction methods based on clusterwise linear regression, (c) assess the influence of geographic regions on the performance of prediction models in predicting monthly and weekly rainfall in Australia, (d) determine the combined influence of meteorological variables on rainfall prediction in Australia, and (e) carry out a comparative analysis of new and existing prediction techniques using Australian rainfall data. In this study, rainfall data with five input meteorological variables from 24 geographically diverse weather stations in Australia, over the period January 1970 to December 2014, have been taken from the Scientific Information for Land Owners (SILO). We also consider the climate zones when selecting weather stations, because Australia experiences a variety of climates due to its size. The data was divided into training and testing periods for evaluation purposes. In this study, optimization based clusterwise linear regression is modified and new prediction methods are developed for rainfall prediction. The proposed method is applied to predict monthly and weekly rainfall. The prediction performance of the clusterwise linear regression method was evaluated by comparing observed and predicted rainfall values using the performance measures: root mean squared error, the mean absolute error, the mean absolute scaled error and the Nash-Sutclie coefficient of efficiency. The proposed method is also compared with the clusterwise linear regression based on the maximum likelihood estimation, linear support vector machines for regression, support vector machines for regression with radial basis kernel function, multiple linear regression, artificial neural networks with and without hidden layer and k-nearest neighbours methods using computational results. Initially, to determine the appropriate input variables to be used in the investigation, we assessed all combinations of meteorological variables. The results confirm that single meteorological variables alone are unable to predict rainfall accurately. The prediction performance of all selected models was…
Subjects/Keywords: Accurate rainfall prediction; Clusterwise linear regression; Australia
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Export
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Manager
APA (6th Edition):
Mahmood, A. (2017). Rainfall prediction in Australia : Clusterwise linear regression approach. (Doctoral Dissertation). Federation University Australia. Retrieved from http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/159251 ; https://library.federation.edu.au/record=b2722196
Chicago Manual of Style (16th Edition):
Mahmood, Arshad. “Rainfall prediction in Australia : Clusterwise linear regression approach.” 2017. Doctoral Dissertation, Federation University Australia. Accessed October 24, 2019.
http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/159251 ; https://library.federation.edu.au/record=b2722196.
MLA Handbook (7th Edition):
Mahmood, Arshad. “Rainfall prediction in Australia : Clusterwise linear regression approach.” 2017. Web. 24 Oct 2019.
Vancouver:
Mahmood A. Rainfall prediction in Australia : Clusterwise linear regression approach. [Internet] [Doctoral dissertation]. Federation University Australia; 2017. [cited 2019 Oct 24].
Available from: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/159251 ; https://library.federation.edu.au/record=b2722196.
Council of Science Editors:
Mahmood A. Rainfall prediction in Australia : Clusterwise linear regression approach. [Doctoral Dissertation]. Federation University Australia; 2017. Available from: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/159251 ; https://library.federation.edu.au/record=b2722196
University of Waikato
17.
Liu, Liang.
Linear Genetic Programming with Experience
.
Degree: 2015, University of Waikato
URL: http://hdl.handle.net/10289/9762
► A novel method of using Machine Learning (ML) algorithms to improve the performance of Linear Genetic Programming (LGP) is studied. In this study, structures used…
(more)
▼ A novel method of using Machine Learning (ML) algorithms to improve the performance of
Linear Genetic Programming (LGP) is studied. In this study, structures used to organize the trained ML models are called Experience Models (EM). They are used for different mutate actions of the mutation operator in LGP. The purpose of using EM is to regulate the random search performed by the mutation operator. The aim of using EMs is to let the suitable candidates have higher chances to be selected.
In this study, two sources of knowledge are used to create the training sets that are used to train ML models. The first source is the pre-existing knowledge of symbolic
regression. This knowledge reflects the effect of adding one math function segment to another math function segment. The second source is the knowledge generated during the evolution of LGP. This knowledge reflects the effect of using different gene components at different chromosome indexes on the overall fitness. Based on these two sources of knowledge, two types of EM are designed. They are Static Model (SM) and Dynamic Model (DM). The SM uses ML models trained with the first knowledge source. A SM tries to achieve the aim of using an EM by reducing the size of the candidate sets used by the increase action of the mutation operator. The DM uses ML models trained with the second knowledge source. A DM tries to achieve the aim of using an EM by creating distributions of gene component types, which can reflect the information in the second knowledge source, for change action of the mutation operator. In this study, SM is used only for increase action in the mutation operator; DM is used only for change action in the mutation operator.
From the experiment results, if compared with a LGP, when a LGP using a SM, it tends to need fewer generations to have a hit, at the same time achieving similar mean best fitness. In contrary, when used with a DM, a LGP do not show performance improvements.
Advisors/Committee Members: Mayo, Michael (advisor).
Subjects/Keywords: Linear Genetic Programming;
Machine Learning;
Symbolic Regression
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APA (6th Edition):
Liu, L. (2015). Linear Genetic Programming with Experience
. (Masters Thesis). University of Waikato. Retrieved from http://hdl.handle.net/10289/9762
Chicago Manual of Style (16th Edition):
Liu, Liang. “Linear Genetic Programming with Experience
.” 2015. Masters Thesis, University of Waikato. Accessed October 24, 2019.
http://hdl.handle.net/10289/9762.
MLA Handbook (7th Edition):
Liu, Liang. “Linear Genetic Programming with Experience
.” 2015. Web. 24 Oct 2019.
Vancouver:
Liu L. Linear Genetic Programming with Experience
. [Internet] [Masters thesis]. University of Waikato; 2015. [cited 2019 Oct 24].
Available from: http://hdl.handle.net/10289/9762.
Council of Science Editors:
Liu L. Linear Genetic Programming with Experience
. [Masters Thesis]. University of Waikato; 2015. Available from: http://hdl.handle.net/10289/9762
18.
Hamal, Tamanna.
Comparing discriminant analysis and linear regression analysis to predict the alcohol consumption by high school students.
Degree: MS, Mathematics, 2017, Texas Woman's University
URL: http://hdl.handle.net/11274/9356
► The purpose of the study is to compare the Discriminant Analysis and Linear Regression Analysis to predict the correlation between alcohol consumption by high school…
(more)
▼ The purpose of the study is to compare the Discriminant Analysis and
Linear Regression Analysis to predict the correlation between alcohol consumption by high school students and their social attributes and grades. Discriminant Analysis, developed by R. A. Fisher in 1936, is a statistical technique used to determine which variables discriminate between two or more mutually exclusive naturally occurring groups.
Linear Regression Analysis is the most widely used statistical technique where straight lines are fitted to patterns of data. In this model, the dependent variable, the variable of interest, is predicted from independent variables using a
linear equation. Even though the earliest form of
linear regression was the Method of Least Squares, which was published by Legendre in 1805, and by Gauss in 1809, the term
regression was pioneered by Sir Francis Galton.
Regression analysis is the process of finding out the relationship between one or more dependent variables and the independent variables.
Advisors/Committee Members: Marshall, David (Committee Chair), Falley, Brandi (committee member), Edwards, Don (committee member).
Subjects/Keywords: Pure sciences; Analysis; Discriminant; Linear regression; SPSS
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APA (6th Edition):
Hamal, T. (2017). Comparing discriminant analysis and linear regression analysis to predict the alcohol consumption by high school students. (Masters Thesis). Texas Woman's University. Retrieved from http://hdl.handle.net/11274/9356
Chicago Manual of Style (16th Edition):
Hamal, Tamanna. “Comparing discriminant analysis and linear regression analysis to predict the alcohol consumption by high school students.” 2017. Masters Thesis, Texas Woman's University. Accessed October 24, 2019.
http://hdl.handle.net/11274/9356.
MLA Handbook (7th Edition):
Hamal, Tamanna. “Comparing discriminant analysis and linear regression analysis to predict the alcohol consumption by high school students.” 2017. Web. 24 Oct 2019.
Vancouver:
Hamal T. Comparing discriminant analysis and linear regression analysis to predict the alcohol consumption by high school students. [Internet] [Masters thesis]. Texas Woman's University; 2017. [cited 2019 Oct 24].
Available from: http://hdl.handle.net/11274/9356.
Council of Science Editors:
Hamal T. Comparing discriminant analysis and linear regression analysis to predict the alcohol consumption by high school students. [Masters Thesis]. Texas Woman's University; 2017. Available from: http://hdl.handle.net/11274/9356
Texas A&M University
19.
Van Huyck, Carl Phillips.
A Matrix Based Approach for Color Transformations in Reflections.
Degree: MS, Visualization, 2018, Texas A&M University
URL: http://hdl.handle.net/1969.1/173569
► In this thesis, I demonstrate the feasibility of linear regression with 4 × 4 matrices to perform color transformations, specifically looking at the case of…
(more)
▼ In this thesis, I demonstrate the feasibility of
linear regression with 4 × 4 matrices to perform color transformations, specifically looking at the case of color transformations in reflections. I compare and analyze the power and performance
linear regression models based on 3 × 3 and 4 × 4 matrices. I conclude that using 4 × 4 matrices in
linear regression is more advantageous in power and performance over using 3 × 3 matrices in
linear regressions, as 4 × 4 matrices allow for categorically more transformations by including the possibility of translation. This provides more general affine transformations to a color space, rather than being restricted to passing through the origin. I examine the benefits of allowing for negative elements in color transformation matrices. I also touch on the possible differences in application between filled 4 × 4 matrices and diagonal 4 × 4 matrices, and discuss the limitations inherent to
linear regression used in any type of matrix operations.
Advisors/Committee Members: Akleman, Ergun (advisor), Shell, Dylan (committee member), Davison, Richard (committee member), Furuta, Richard (committee member).
Subjects/Keywords: Reflections; Computer Graphics; Color; Matrices; Linear Regression
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APA (6th Edition):
Van Huyck, C. P. (2018). A Matrix Based Approach for Color Transformations in Reflections. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/173569
Chicago Manual of Style (16th Edition):
Van Huyck, Carl Phillips. “A Matrix Based Approach for Color Transformations in Reflections.” 2018. Masters Thesis, Texas A&M University. Accessed October 24, 2019.
http://hdl.handle.net/1969.1/173569.
MLA Handbook (7th Edition):
Van Huyck, Carl Phillips. “A Matrix Based Approach for Color Transformations in Reflections.” 2018. Web. 24 Oct 2019.
Vancouver:
Van Huyck CP. A Matrix Based Approach for Color Transformations in Reflections. [Internet] [Masters thesis]. Texas A&M University; 2018. [cited 2019 Oct 24].
Available from: http://hdl.handle.net/1969.1/173569.
Council of Science Editors:
Van Huyck CP. A Matrix Based Approach for Color Transformations in Reflections. [Masters Thesis]. Texas A&M University; 2018. Available from: http://hdl.handle.net/1969.1/173569
Kansas State University
20.
Cao, Chendi.
Linear
regression with Laplace measurement error.
Degree: MS, Statistics, 2016, Kansas State University
URL: http://hdl.handle.net/2097/32719
► In this report, an improved estimation procedure for the regression parameter in simple linear regression models with the Laplace measurement error is proposed. The estimation…
(more)
▼ In this report, an improved estimation procedure for
the
regression parameter in simple
linear regression models with
the Laplace measurement error is proposed. The estimation procedure
is made feasible by a Tweedie type equality established for E(X|Z),
where Z = X + U, X and U are independent, and U follows a Laplace
distribution. When the density function of X is unknown, a kernel
estimator for E(X|Z) is constructed in the estimation procedure. A
leave-one-out cross validation bandwidth selection method is
designed. The finite sample performance of the proposed estimation
procedure is evaluated by simulation studies. Comparison study is
also conducted to show the superiority of the proposed estimation
procedure over some existing estimation methods.
Advisors/Committee Members: Weixing Song.
Subjects/Keywords: Measurement
error; Laplace
distribution; Linear
regression
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APA (6th Edition):
Cao, C. (2016). Linear
regression with Laplace measurement error. (Masters Thesis). Kansas State University. Retrieved from http://hdl.handle.net/2097/32719
Chicago Manual of Style (16th Edition):
Cao, Chendi. “Linear
regression with Laplace measurement error.” 2016. Masters Thesis, Kansas State University. Accessed October 24, 2019.
http://hdl.handle.net/2097/32719.
MLA Handbook (7th Edition):
Cao, Chendi. “Linear
regression with Laplace measurement error.” 2016. Web. 24 Oct 2019.
Vancouver:
Cao C. Linear
regression with Laplace measurement error. [Internet] [Masters thesis]. Kansas State University; 2016. [cited 2019 Oct 24].
Available from: http://hdl.handle.net/2097/32719.
Council of Science Editors:
Cao C. Linear
regression with Laplace measurement error. [Masters Thesis]. Kansas State University; 2016. Available from: http://hdl.handle.net/2097/32719
University of Arizona
21.
Kaye, Justin.
Warfarin Pharmacogenomics in a Hispanic Population: A Candidate SNP Study
.
Degree: 2019, University of Arizona
URL: http://hdl.handle.net/10150/633086
► Background: Warfarin remains one of the most widely prescribed anticoagulants but is also a leading cause of adverse drug reactions. Genotype-guided warfarin dosing algorithms enable…
(more)
▼ Background: Warfarin remains one of the most widely prescribed anticoagulants but is also a leading cause of adverse drug reactions. Genotype-guided warfarin dosing algorithms enable accurate dose estimation, potentially leading to improved safety and efficacy. However, genotype-guided dosing algorithms were developed primarily in populations of European descent and limited data are available regarding single nucleotide polymorphisms (SNPs) that significantly influence warfarin dose in Hispanic populations.
Research Aim: The objective of this study was to determine whether clinical factors and SNPs previously associated with stable warfarin dose variability in populations of European and Hispanic descent accurately predicted warfarin stable dose in a Hispanic population.
Study Design: Self-reported Hispanic and Latino patients on stable warfarin dose (defined as the same dose for at least two clinic visits separated by at least two weeks) were recruited.
Methods: Candidate SNPs, including CYP2C9*2/*3, VKORC1-1639G>A, CYP4F2*3, and NQO1*2, were genotyped and clinical data were collected using a survey and the electronic medical record. Stepwise
linear regression was performed to determine variables that significantly predicted square root of weekly warfarin dose.
Results: A total of 76 patients of primarily Mexican American ancestry participated. All SNPs were within Hardy-Weinberg Equilibrium. The final stepwise
regression model incorporated six variables, which explained 71% of the variability in warfarin weekly dose requirements. Significant predictors included weight (R2=0.287, p<0.0001), age (R2=0.143, p<0.0001), amiodarone use (R2=0.067, p=0.0005), and prior stroke (R2=0.025, p=0.02). Significant SNPs included VKORC1-1639A (R2=0.152, p<0.0001), and CYP2C9*2/*3 (R2=0.032, p=0.02). CYP4F2*3 and NQO1*2 did not significantly impact warfarin dose requirements despite previously published associations in Hispanic populations.
Conclusion: These findings suggest that clinical and genetic predictors of warfarin weekly dose requirements are similar among populations of European descent and Hispanic populations with Mexican American ancestry. These results require replication and validation in independent cohorts with similar ethnicity, but advance our understanding of influences on warfarin dose variability among different race/ethnic groups.
Advisors/Committee Members: Karnes, Jason H (advisor), Laukaitis, Christina M. (committeemember), Darnell, Diana K. (committeemember).
Subjects/Keywords: Hispanic;
Latino;
linear regression;
Pharmacogenomics;
SNP;
Warfarin
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Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Kaye, J. (2019). Warfarin Pharmacogenomics in a Hispanic Population: A Candidate SNP Study
. (Masters Thesis). University of Arizona. Retrieved from http://hdl.handle.net/10150/633086
Chicago Manual of Style (16th Edition):
Kaye, Justin. “Warfarin Pharmacogenomics in a Hispanic Population: A Candidate SNP Study
.” 2019. Masters Thesis, University of Arizona. Accessed October 24, 2019.
http://hdl.handle.net/10150/633086.
MLA Handbook (7th Edition):
Kaye, Justin. “Warfarin Pharmacogenomics in a Hispanic Population: A Candidate SNP Study
.” 2019. Web. 24 Oct 2019.
Vancouver:
Kaye J. Warfarin Pharmacogenomics in a Hispanic Population: A Candidate SNP Study
. [Internet] [Masters thesis]. University of Arizona; 2019. [cited 2019 Oct 24].
Available from: http://hdl.handle.net/10150/633086.
Council of Science Editors:
Kaye J. Warfarin Pharmacogenomics in a Hispanic Population: A Candidate SNP Study
. [Masters Thesis]. University of Arizona; 2019. Available from: http://hdl.handle.net/10150/633086
Victoria University of Wellington
22.
Bancolita, Joel E.
Poverty Diagnostics in the Philippines: Assessing Impacts of Programs through Generalised Linear Models (GLMs).
Degree: 2011, Victoria University of Wellington
URL: http://hdl.handle.net/10063/1913
► The Philippines is a country where a quarter to one-third of the population is poor. Although the nation has managed to lower poverty incidence in…
(more)
▼ The Philippines is a country where a quarter to one-third of the population
is poor. Although the nation has managed to lower poverty incidence
in some years, its booming population increases the poor population dramatically.
This is why alleviating poverty is a pinnacle program in the
country.
In aid of poverty alleviation endeavor, this study focuses on assessing
which programs had been effective in alleviating poverty given other
family characteristics. Aside from descriptive methods, employing Generalised
Linear Models (GLMs) and categorical data analysis are the focus
in analysing the effects of existing intervention programs on status of
improvement and income of families. In addition, varying effects of programs
depending on values of other covariates are also analysed.
Descriptive analysis and modeling are applied on the panel data of
families. Intervention programs namely scholarship, Comprehensive Agrarian
Reform Program (CARP) and government housing or other housing financing
program (GHFP) have been run together with other family characteristics
to describe improvement in welfare and income. Interaction
effects, between access to intervention programs and other aspects of the
family, have been derived to give a richer picture of the phenomenon. The
study has come to conclude that the programs are indeed effective in improving
lives of families, with some effects varying on some levels of other
explanatory variables.
Advisors/Committee Members: Hirose, Yuichi, Liu, I-Ming.
Subjects/Keywords: Generalized linear models; Linear regression; Logistic regression; Logit; Odds ratio; Poverty; GLM
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Manager
APA (6th Edition):
Bancolita, J. E. (2011). Poverty Diagnostics in the Philippines: Assessing Impacts of Programs through Generalised Linear Models (GLMs). (Masters Thesis). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/1913
Chicago Manual of Style (16th Edition):
Bancolita, Joel E. “Poverty Diagnostics in the Philippines: Assessing Impacts of Programs through Generalised Linear Models (GLMs).” 2011. Masters Thesis, Victoria University of Wellington. Accessed October 24, 2019.
http://hdl.handle.net/10063/1913.
MLA Handbook (7th Edition):
Bancolita, Joel E. “Poverty Diagnostics in the Philippines: Assessing Impacts of Programs through Generalised Linear Models (GLMs).” 2011. Web. 24 Oct 2019.
Vancouver:
Bancolita JE. Poverty Diagnostics in the Philippines: Assessing Impacts of Programs through Generalised Linear Models (GLMs). [Internet] [Masters thesis]. Victoria University of Wellington; 2011. [cited 2019 Oct 24].
Available from: http://hdl.handle.net/10063/1913.
Council of Science Editors:
Bancolita JE. Poverty Diagnostics in the Philippines: Assessing Impacts of Programs through Generalised Linear Models (GLMs). [Masters Thesis]. Victoria University of Wellington; 2011. Available from: http://hdl.handle.net/10063/1913
North-West University
23.
Van der Westhuizen, Magdelena Marianna.
Robust techniques for regression models with minimal assumptions / M.M. van der Westhuizen
.
Degree: 2011, North-West University
URL: http://hdl.handle.net/10394/6689
► Good quality management decisions often rely on the evaluation and interpretation of data. One of the most popular ways to investigate possible relationships in a…
(more)
▼ Good quality management decisions often rely on the evaluation and interpretation of data. One of the most popular ways to investigate possible relationships in a given data set is to follow a process of fitting models to the data. Regression models are often employed to assist with decision making. In addition to decision making, regression models can also be used for the optimization and prediction of data. The success of a regression model, however, relies heavily on assumptions made by the model builder. In addition, the model may also be influenced by the presence of outliers; a more robust model, which is not as easily affected by outliers, is necessary in making more accurate interpretations about the data. In this research study robust techniques for regression models with minimal assumptions are explored. Mathematical programming techniques such as linear programming, mixed integer linear programming, and piecewise linear regression are used to formulate a nonlinear regression model. Outlier detection and smoothing techniques are included to address the robustness of the model and to improve predictive accuracy. The performance of the model is tested by applying it to a variety of data sets and comparing the results to those of other models. The results of the empirical experiments are also presented in this study.
Subjects/Keywords: Robust regression;
Outlier detection;
Piecewise linear regression;
Linear programming;
Smoothing techniques;
Optimization
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to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Van der Westhuizen, M. M. (2011). Robust techniques for regression models with minimal assumptions / M.M. van der Westhuizen
. (Thesis). North-West University. Retrieved from http://hdl.handle.net/10394/6689
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Van der Westhuizen, Magdelena Marianna. “Robust techniques for regression models with minimal assumptions / M.M. van der Westhuizen
.” 2011. Thesis, North-West University. Accessed October 24, 2019.
http://hdl.handle.net/10394/6689.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Van der Westhuizen, Magdelena Marianna. “Robust techniques for regression models with minimal assumptions / M.M. van der Westhuizen
.” 2011. Web. 24 Oct 2019.
Vancouver:
Van der Westhuizen MM. Robust techniques for regression models with minimal assumptions / M.M. van der Westhuizen
. [Internet] [Thesis]. North-West University; 2011. [cited 2019 Oct 24].
Available from: http://hdl.handle.net/10394/6689.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Van der Westhuizen MM. Robust techniques for regression models with minimal assumptions / M.M. van der Westhuizen
. [Thesis]. North-West University; 2011. Available from: http://hdl.handle.net/10394/6689
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Pretoria
24.
Van Zyl, W.R. (Warren Reece).
Single-Phase
convective heat transfer and pressure drop coefficients in
concentric annual.
Degree: Mechanical and Aeronautical
Engineering, 2013, University of Pretoria
URL: http://hdl.handle.net/2263/33350
► Varying diameter ratios associated with smooth concentric tube-in-tube heat exchangers are known to have an effect on its convective heat transfer capabilities. Much literature exists…
(more)
▼ Varying diameter ratios associated with smooth
concentric tube-in-tube heat exchangers
are known to have an
effect on its convective heat transfer capabilities. Much
literature
exists for predicting the inner tube’s heat transfer
coefficients, however, limited research
has been conducted for the
annulus and some of the existing correlations are known to
have
large errors.
Linear and nonlinear
regression models exist for
determining the heat transfer coefficients,
however, these are
complex and time consuming methods and require much experimental
data in order to obtain accurate solutions. A direct solution to
obtain the heat transfer
coefficients in the annulus is sought
after.
In this study a large dataset of experimental measurements
on heat exchangers with
annular diameter ratios of 0.483, 0.579,
0.593 and 0.712 was gathered. The annular
diameter ratio is
defined as the ratio of the outer diameter of the inner tube to the
inner
diameter of the outer tube. Using various methods, the data
was processed to determine
local and average Nusselt numbers in
the turbulent flow regime. These methods included
the modified
Wilson plot technique, a nonlinear
regression scheme, as well as
the log mean
temperature difference method. The inner tube
Reynolds number exponent was assumed
to be a constant 0.8 for both
the modified Wilson plot and nonlinear
regression methods.
The
logarithmic mean temperature difference method was used for both a
mean analysis on
the full length of the heat exchanger, and a
local analysis on finite control volumes. Friction
factors were
calculated directly from measured pressure drops across the
annuli.
The heat exchangers were tested for both a heated and
cooled annulus, and arranged in a
horizontal counter-flow
configuration with water as the working medium. Data was
gathered
for Reynolds numbers (based on the hydraulic diameter) varying from
10 000 to 28
000 for a heated annulus and 10 000 to 45 000 for a
cooled annulus. Local inner wall
temperatures which are generally
difficult to determine, were measured with
thermocouples embedded
within the wall. Flow obstructions within the annuli were
minimized, with only the support structures maintaining
concentricity of the inner and outer
tubes impeding
flow.
Advisors/Committee Members: Dirker, Jaco (advisor), Meyer, Josua P. (coadvisor).
Subjects/Keywords: Energy; Heat
transfer; Nonlinear
regression models; Linear
regression models;
UCTD
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MLA ·
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Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Van Zyl, W. R. (. R. (2013). Single-Phase
convective heat transfer and pressure drop coefficients in
concentric annual. (Masters Thesis). University of Pretoria. Retrieved from http://hdl.handle.net/2263/33350
Chicago Manual of Style (16th Edition):
Van Zyl, W R (Warren Reece). “Single-Phase
convective heat transfer and pressure drop coefficients in
concentric annual.” 2013. Masters Thesis, University of Pretoria. Accessed October 24, 2019.
http://hdl.handle.net/2263/33350.
MLA Handbook (7th Edition):
Van Zyl, W R (Warren Reece). “Single-Phase
convective heat transfer and pressure drop coefficients in
concentric annual.” 2013. Web. 24 Oct 2019.
Vancouver:
Van Zyl WR(R. Single-Phase
convective heat transfer and pressure drop coefficients in
concentric annual. [Internet] [Masters thesis]. University of Pretoria; 2013. [cited 2019 Oct 24].
Available from: http://hdl.handle.net/2263/33350.
Council of Science Editors:
Van Zyl WR(R. Single-Phase
convective heat transfer and pressure drop coefficients in
concentric annual. [Masters Thesis]. University of Pretoria; 2013. Available from: http://hdl.handle.net/2263/33350
NSYSU
25.
Ching, Yi-Ching.
Factors Affecting the Carrying Capacity of a Rapid Transit System: The Case Study of Kaohsiung Rapid Transit System.
Degree: Master, Economics, 2014, NSYSU
URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0629114-143301
► This study develops a multiple regression model and then applies the method of stepwise regression to study the factors affecting the carrying capacity of Kaohsiung…
(more)
▼ This study develops a multiple
regression model and then applies the method
of stepwise
regression to study the factors affecting the carrying capacity
of Kaohsiung rapid transit system. Based on the period from April,2008 to
November,2013, the empirical results indicate that population, tourists,
hospital and business setup items of Kaohsiung city have significant
positive impacts on the carrying capacity of Kaohsiung rapid transit system.
The developed
regression model has good explanatory power.
Advisors/Committee Members: Hu,YuHau (chair), Wu, Jyh-Lin (committee member), Shu-Ling Chiang (chair).
Subjects/Keywords: stepwise regression; carrying capacity; ordinary least squares; Multiple linear regression
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to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Ching, Y. (2014). Factors Affecting the Carrying Capacity of a Rapid Transit System: The Case Study of Kaohsiung Rapid Transit System. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0629114-143301
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Ching, Yi-Ching. “Factors Affecting the Carrying Capacity of a Rapid Transit System: The Case Study of Kaohsiung Rapid Transit System.” 2014. Thesis, NSYSU. Accessed October 24, 2019.
http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0629114-143301.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Ching, Yi-Ching. “Factors Affecting the Carrying Capacity of a Rapid Transit System: The Case Study of Kaohsiung Rapid Transit System.” 2014. Web. 24 Oct 2019.
Vancouver:
Ching Y. Factors Affecting the Carrying Capacity of a Rapid Transit System: The Case Study of Kaohsiung Rapid Transit System. [Internet] [Thesis]. NSYSU; 2014. [cited 2019 Oct 24].
Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0629114-143301.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Ching Y. Factors Affecting the Carrying Capacity of a Rapid Transit System: The Case Study of Kaohsiung Rapid Transit System. [Thesis]. NSYSU; 2014. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0629114-143301
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Ohio University
26.
Khajuria, Saket.
A Model to Predict Student Matriculation from Admissions
Data.
Degree: MS, Industrial and Manufacturing Systems Engineering
(Engineering), 2007, Ohio University
URL: http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1167852960
► Enrollment in a university can be increased by changing any of the following three attributes: the applicant pool size, the marketing strategies for applicants,…
(more)
▼ Enrollment in a university can be increased
by changing any of the following three attributes: the applicant
pool size, the marketing strategies for applicants, or the
admission standards. Universities are using different predictive
modeling techniques to increase enrollment, given their changing
demographics, tuition rates, and other factors. This document
presents a model for predicting the likelihood that a specific
undergraduate applicant will matriculate if admitted. A
regression
model was built using data on the applicants for the 2004 freshman
class. Using this model, applicants from 2005 were evaluated, and
matriculation was predicted. Applicants predicted to matriculate
did so 47.91% of the time, while students projected not to
matriculate only matriculated 28.05% of the time. Considering all
four possible outcomes (correct matriculation prediction to
incorrect non-matriculation decision), the overall accuracy was
60.2%. The accuracy of the model was similar to studies in the
literature.
Advisors/Committee Members: Koonce, David (Advisor).
Subjects/Keywords: Engineering, Industrial; Predictive Enrollment Modelling; Linear Regression; Logistic Regression
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APA (6th Edition):
Khajuria, S. (2007). A Model to Predict Student Matriculation from Admissions
Data. (Masters Thesis). Ohio University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1167852960
Chicago Manual of Style (16th Edition):
Khajuria, Saket. “A Model to Predict Student Matriculation from Admissions
Data.” 2007. Masters Thesis, Ohio University. Accessed October 24, 2019.
http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1167852960.
MLA Handbook (7th Edition):
Khajuria, Saket. “A Model to Predict Student Matriculation from Admissions
Data.” 2007. Web. 24 Oct 2019.
Vancouver:
Khajuria S. A Model to Predict Student Matriculation from Admissions
Data. [Internet] [Masters thesis]. Ohio University; 2007. [cited 2019 Oct 24].
Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1167852960.
Council of Science Editors:
Khajuria S. A Model to Predict Student Matriculation from Admissions
Data. [Masters Thesis]. Ohio University; 2007. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1167852960
University of Wollongong
27.
Riyapan, Preeya.
Model choices for complex survey analysis.
Degree: PhD, 2016, University of Wollongong
URL: ;
https://ro.uow.edu.au/theses/4670
► Survey data are an important source of information for modern society. However, the complex structures of modern populations require sampling designs for surveys that…
(more)
▼ Survey data are an important source of information for modern society. However, the complex structures of modern populations require sampling designs for surveys that are more complex than simple random sampling in order to be effective. With large national population surveys, the sample data collected via these designs typically include sample weights that allow analysis to take account of these complex population structures. As a consequence, these sample weights need to be taken into consideration when modelling the sample data, e.g. when the target of estimation is the coefficients of a regression model for the target population. In this situation, it is important to know whether these weights should be used when identifying an appropriate model specification and also whether they should be used when fitting this model to the survey data. Given the complexity of both model choice and model fitting and the limited literature on this issue, there is clearly scope for theoretical and methodological development in order to help with these decisions.
The principal aim of this thesis is to develop and evaluate strategies for population modelling using complex sample survey data. More specifically, since both linear and logistic regression analysis are very widely used statistical modelling methods, our goal is to develop procedures for analysing complex sample survey data in order to choose appropriate linear and logistic regression models based on either unweighted or weighted modelling of the survey data. In particular we develop two approaches to regression model choice and consequent regression model fit given complex survey data. These are a likelihood-based approach and a prediction-based approach. Both approaches allow us to identify a final model given two competing models suggested by model search methods based on application of different inferential paradigms. The likelihood approach is based on the non-nested test suggested by Vuong (1989), while the predictive approach uses cross-validation. The two model choice methods differ in terms of whether or not they use the sample weights. That is, we investigate four modelling strategies defined by the combination of two different approaches to model identification (likelihood-based versus cross-validation) and two paradigms for model search (unweighted versus weighted).
Subjects/Keywords: model choices; complex survey; population modelling; linear regression; logistic regression
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APA (6th Edition):
Riyapan, P. (2016). Model choices for complex survey analysis. (Doctoral Dissertation). University of Wollongong. Retrieved from ; https://ro.uow.edu.au/theses/4670
Chicago Manual of Style (16th Edition):
Riyapan, Preeya. “Model choices for complex survey analysis.” 2016. Doctoral Dissertation, University of Wollongong. Accessed October 24, 2019.
; https://ro.uow.edu.au/theses/4670.
MLA Handbook (7th Edition):
Riyapan, Preeya. “Model choices for complex survey analysis.” 2016. Web. 24 Oct 2019.
Vancouver:
Riyapan P. Model choices for complex survey analysis. [Internet] [Doctoral dissertation]. University of Wollongong; 2016. [cited 2019 Oct 24].
Available from: ; https://ro.uow.edu.au/theses/4670.
Council of Science Editors:
Riyapan P. Model choices for complex survey analysis. [Doctoral Dissertation]. University of Wollongong; 2016. Available from: ; https://ro.uow.edu.au/theses/4670
Rice University
28.
Joshi, Babhru.
A Convex Algorithm for Mixed Linear Regression.
Degree: MA, Engineering, 2017, Rice University
URL: http://hdl.handle.net/1911/95962
► Mixed linear regression is a high dimensional affine space clustering problem where the goal is to find the parameters of multiple affine spaces that best…
(more)
▼ Mixed
linear regression is a high dimensional affine space clustering problem where the goal is to find the parameters of multiple affine spaces that best fit a collection of points. We introduce a convex 2nd order cone program (based on l1/fused lasso) which allows us to reformulate the mixed
linear regression as an Rd clustering problem. The convex program is parameter free and does not require prior knowledge of the number of clusters, which is more tractable while clustering in Rd. In the noiseless case, we prove that the convex program recovers the
regression coefficients exactly under narrow technical conditions of well-separation and balance. We demonstrate numerical performance on BikeShare data and music tone perception data.
Advisors/Committee Members: Hand, Paul E (advisor).
Subjects/Keywords: mixed linear regression; mixed regression; mixture model; fused lasso
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APA (6th Edition):
Joshi, B. (2017). A Convex Algorithm for Mixed Linear Regression. (Masters Thesis). Rice University. Retrieved from http://hdl.handle.net/1911/95962
Chicago Manual of Style (16th Edition):
Joshi, Babhru. “A Convex Algorithm for Mixed Linear Regression.” 2017. Masters Thesis, Rice University. Accessed October 24, 2019.
http://hdl.handle.net/1911/95962.
MLA Handbook (7th Edition):
Joshi, Babhru. “A Convex Algorithm for Mixed Linear Regression.” 2017. Web. 24 Oct 2019.
Vancouver:
Joshi B. A Convex Algorithm for Mixed Linear Regression. [Internet] [Masters thesis]. Rice University; 2017. [cited 2019 Oct 24].
Available from: http://hdl.handle.net/1911/95962.
Council of Science Editors:
Joshi B. A Convex Algorithm for Mixed Linear Regression. [Masters Thesis]. Rice University; 2017. Available from: http://hdl.handle.net/1911/95962
University of Manitoba
29.
Munaweera Arachchilage, Inesh Prabuddha.
Shrinkage estimators under generalized garrote and LINEX loss functions for regression analysis.
Degree: Statistics, 2018, University of Manitoba
URL: http://hdl.handle.net/1993/33380
► Shrinkage methods are widely used in multiple linear regression analysis to address the multicollinearity and some other issues in many practical situations. Most of the…
(more)
▼ Shrinkage methods are widely used in multiple
linear regression analysis to address the multicollinearity and some other issues in many practical situations. Most of the commonly used shrinkage methods such as ridge
regression and lasso do not consider the importance of each variable when applying the shrinkage, and all model coefficients will be shrunken towards zero in a similar rate. In other words, those methods shrink least important coefficients and most important coefficients similarly. When someone needs to perform the subset selection while applying asymmetric shrinkage on coefficients, the adaptive lasso can be used, and if someone wants to retain all the variables in the model he/she can use generalized ridge
regression. However, since the generalized ridge
regression is defined with a transformed design matrix, and since it uses a number of tuning parameters which is equal to the number of predictors in the model, the user has no control over the amount of shrinkage on any individual coefficient. This thesis addresses this issue by proposing some new regularized methods which can be used as alternatives to ridge
regression, following the idea of the non-negative garrote function. First, we develop the quadratic garrote, which shrinks coefficients unequally, and at the same time retains all the variables in the model. We show that this approach, it is capable of shrinking smaller coefficients even faster than the adaptive lasso while keeping the larger coefficients almost untouched. Also, we generalize the quadratic garrote so that it gives the flexibility for the user to decide the level of shrinkage on each variable directly, based on his experience or prior knowledge. We derive a closed-form solution for the quadratic garrote problem and study the theoretical properties of the suggested estimator such as its variance, expectation, and bias.
Furthermore, we explore the possibility of using different loss functions as the penalty in the non-negative garrote and develop the LINEX
regression method as a novel shrinkage approach. We use a numerical optimization technique to estimate the LINEX
regression coefficients. In addition, through simulation studies under different settings as well as real-world applications, we show that the suggested shrinkage method can be used as a better substitute for the ridge
regression in terms of prediction error and practical use.
Advisors/Committee Members: Jafari Jozani, Mohammad (Statistics) Muthukumarana, Saman (Statistics) (supervisor), Wang, Liqun (Statistics) Arino, Julien (Mathematics) (examiningcommittee).
Subjects/Keywords: LINEX regression; Multiple linear regression; Non-negative garrote; Shrinkage methods
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APA (6th Edition):
Munaweera Arachchilage, I. P. (2018). Shrinkage estimators under generalized garrote and LINEX loss functions for regression analysis. (Masters Thesis). University of Manitoba. Retrieved from http://hdl.handle.net/1993/33380
Chicago Manual of Style (16th Edition):
Munaweera Arachchilage, Inesh Prabuddha. “Shrinkage estimators under generalized garrote and LINEX loss functions for regression analysis.” 2018. Masters Thesis, University of Manitoba. Accessed October 24, 2019.
http://hdl.handle.net/1993/33380.
MLA Handbook (7th Edition):
Munaweera Arachchilage, Inesh Prabuddha. “Shrinkage estimators under generalized garrote and LINEX loss functions for regression analysis.” 2018. Web. 24 Oct 2019.
Vancouver:
Munaweera Arachchilage IP. Shrinkage estimators under generalized garrote and LINEX loss functions for regression analysis. [Internet] [Masters thesis]. University of Manitoba; 2018. [cited 2019 Oct 24].
Available from: http://hdl.handle.net/1993/33380.
Council of Science Editors:
Munaweera Arachchilage IP. Shrinkage estimators under generalized garrote and LINEX loss functions for regression analysis. [Masters Thesis]. University of Manitoba; 2018. Available from: http://hdl.handle.net/1993/33380
University of Alberta
30.
Ávila Pires, Bernardo.
Statistical analysis of L1-penalized linear estimation with
applications.
Degree: MS, Department of Computing Science, 2011, University of Alberta
URL: https://era.library.ualberta.ca/files/dr26xz283
► We study linear estimation based on perturbed data when performance is measured by a matrix norm of the expected residual error, in particular, the case…
(more)
▼ We study linear estimation based on perturbed data
when performance is measured by a matrix norm of the expected
residual error, in particular, the case in which there are many
unknowns, but the “best” estimator is sparse, or has small L1-norm.
We propose a Lasso-like procedure that finds the minimizer of an
L1-penalized squared norm of the residual. For linear regression we
show O(sqrt(1/n)) uniform bounds for the difference between the
residual error norm of our estimator and that of the “best”
estimator. These also hold for on-policy value function
approximation in reinforcement learning. In the off-policy case, we
show O(sqrt((ln n)/n)) bounds for the expected difference. Our
analysis has a unique feature: it is the same for both regression
and reinforcement learning. We took care to separate the
deterministic and probabilistic arguments, so as to analyze a range
of seemingly different linear estimation problems in a unified
way.
Subjects/Keywords: linear estimation; linear regression; machine learning; Lasso; excess risk; reinforcement learning
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APA ·
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APA (6th Edition):
Ávila Pires, B. (2011). Statistical analysis of L1-penalized linear estimation with
applications. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/dr26xz283
Chicago Manual of Style (16th Edition):
Ávila Pires, Bernardo. “Statistical analysis of L1-penalized linear estimation with
applications.” 2011. Masters Thesis, University of Alberta. Accessed October 24, 2019.
https://era.library.ualberta.ca/files/dr26xz283.
MLA Handbook (7th Edition):
Ávila Pires, Bernardo. “Statistical analysis of L1-penalized linear estimation with
applications.” 2011. Web. 24 Oct 2019.
Vancouver:
Ávila Pires B. Statistical analysis of L1-penalized linear estimation with
applications. [Internet] [Masters thesis]. University of Alberta; 2011. [cited 2019 Oct 24].
Available from: https://era.library.ualberta.ca/files/dr26xz283.
Council of Science Editors:
Ávila Pires B. Statistical analysis of L1-penalized linear estimation with
applications. [Masters Thesis]. University of Alberta; 2011. Available from: https://era.library.ualberta.ca/files/dr26xz283
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Open Access Theses and Dissertations
