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You searched for subject:(solution path case weight homotopy data perturbation optimism large margin classifier svm quantile regression). Showing records 1 – 30 of 69946 total matches.

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The Ohio State University

1. TU, SHANSHAN. Case Influence and Model Complexity in Regression and Classification.

Degree: PhD, Statistics, 2019, The Ohio State University

Case influence and model complexity play very important roles in model diagnostics and model comparison. They have been extensively studied in linear regression and generalized… (more)

Subjects/Keywords: Statistics; solution path, case weight, homotopy, data perturbation, optimism, large margin classifier, svm, quantile regression

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

TU, S. (2019). Case Influence and Model Complexity in Regression and Classification. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1563324139376977

Chicago Manual of Style (16th Edition):

TU, SHANSHAN. “Case Influence and Model Complexity in Regression and Classification.” 2019. Doctoral Dissertation, The Ohio State University. Accessed November 17, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1563324139376977.

MLA Handbook (7th Edition):

TU, SHANSHAN. “Case Influence and Model Complexity in Regression and Classification.” 2019. Web. 17 Nov 2019.

Vancouver:

TU S. Case Influence and Model Complexity in Regression and Classification. [Internet] [Doctoral dissertation]. The Ohio State University; 2019. [cited 2019 Nov 17]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1563324139376977.

Council of Science Editors:

TU S. Case Influence and Model Complexity in Regression and Classification. [Doctoral Dissertation]. The Ohio State University; 2019. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1563324139376977


University of Minnesota

2. Peng, Bo. Methodologies and Algorithms on Some Non-convex Penalized Models for Ultra High Dimensional Data.

Degree: PhD, Statistics, 2016, University of Minnesota

 In recent years, penalized models have gained considerable importance on deal- ing with variable selection and estimation problems under high dimensional settings. Of all the… (more)

Subjects/Keywords: High dimensional data; Non-convex penalty; Oracle property; Quantile regression; SVM

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APA (6th Edition):

Peng, B. (2016). Methodologies and Algorithms on Some Non-convex Penalized Models for Ultra High Dimensional Data. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/182177

Chicago Manual of Style (16th Edition):

Peng, Bo. “Methodologies and Algorithms on Some Non-convex Penalized Models for Ultra High Dimensional Data.” 2016. Doctoral Dissertation, University of Minnesota. Accessed November 17, 2019. http://hdl.handle.net/11299/182177.

MLA Handbook (7th Edition):

Peng, Bo. “Methodologies and Algorithms on Some Non-convex Penalized Models for Ultra High Dimensional Data.” 2016. Web. 17 Nov 2019.

Vancouver:

Peng B. Methodologies and Algorithms on Some Non-convex Penalized Models for Ultra High Dimensional Data. [Internet] [Doctoral dissertation]. University of Minnesota; 2016. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/11299/182177.

Council of Science Editors:

Peng B. Methodologies and Algorithms on Some Non-convex Penalized Models for Ultra High Dimensional Data. [Doctoral Dissertation]. University of Minnesota; 2016. Available from: http://hdl.handle.net/11299/182177


The Ohio State University

3. Yao, Yonggang. Statistical Applications of Linear Programming for Feature Selection via Regularization Methods.

Degree: PhD, Statistics, 2008, The Ohio State University

 We consider statistical procedures for feature selection defined by a family of regularizationproblems with convex piecewise linear loss functions and penalties of <i>l</i>1 or <i>l</i>8(more)

Subjects/Keywords: Statistics; variable selection; grouped variable selection; statistical regularization; parametric linear programming; simplex algorithm; tableau-simplex; solution path; support vector machine; quantile regression; Dantzig selector; functional component pursuit

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APA (6th Edition):

Yao, Y. (2008). Statistical Applications of Linear Programming for Feature Selection via Regularization Methods. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1222035715

Chicago Manual of Style (16th Edition):

Yao, Yonggang. “Statistical Applications of Linear Programming for Feature Selection via Regularization Methods.” 2008. Doctoral Dissertation, The Ohio State University. Accessed November 17, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1222035715.

MLA Handbook (7th Edition):

Yao, Yonggang. “Statistical Applications of Linear Programming for Feature Selection via Regularization Methods.” 2008. Web. 17 Nov 2019.

Vancouver:

Yao Y. Statistical Applications of Linear Programming for Feature Selection via Regularization Methods. [Internet] [Doctoral dissertation]. The Ohio State University; 2008. [cited 2019 Nov 17]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1222035715.

Council of Science Editors:

Yao Y. Statistical Applications of Linear Programming for Feature Selection via Regularization Methods. [Doctoral Dissertation]. The Ohio State University; 2008. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1222035715


University of Minnesota

4. Gu, Yuwen. Unconventional Regression for High-Dimensional Data Analysis.

Degree: PhD, Statistics, 2017, University of Minnesota

 Massive and complex data present new challenges that conventional sparse penalized mean regressions, such as the penalized least squares, cannot fully solve. For example, in… (more)

Subjects/Keywords: ADMM; Asymmetric least squares; Composite quantile regression; High-dimensional data; Quantile regression; Sparse penalized regression

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APA (6th Edition):

Gu, Y. (2017). Unconventional Regression for High-Dimensional Data Analysis. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/206270

Chicago Manual of Style (16th Edition):

Gu, Yuwen. “Unconventional Regression for High-Dimensional Data Analysis.” 2017. Doctoral Dissertation, University of Minnesota. Accessed November 17, 2019. http://hdl.handle.net/11299/206270.

MLA Handbook (7th Edition):

Gu, Yuwen. “Unconventional Regression for High-Dimensional Data Analysis.” 2017. Web. 17 Nov 2019.

Vancouver:

Gu Y. Unconventional Regression for High-Dimensional Data Analysis. [Internet] [Doctoral dissertation]. University of Minnesota; 2017. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/11299/206270.

Council of Science Editors:

Gu Y. Unconventional Regression for High-Dimensional Data Analysis. [Doctoral Dissertation]. University of Minnesota; 2017. Available from: http://hdl.handle.net/11299/206270


NSYSU

5. Ke, Yi-syuan. Forecast the M&A targets in IT industry using Support Vector Machine.

Degree: Master, Information Management, 2017, NSYSU

 In this era, companies are able to occupy a place in this vast market, but many companies face the change of market. They will lose… (more)

Subjects/Keywords: M&A; Logistic Regression; Financial data; M&A-SVM model; SVM

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APA (6th Edition):

Ke, Y. (2017). Forecast the M&A targets in IT industry using Support Vector Machine. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0530117-140725

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):

Ke, Yi-syuan. “Forecast the M&A targets in IT industry using Support Vector Machine.” 2017. Thesis, NSYSU. Accessed November 17, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0530117-140725.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Ke, Yi-syuan. “Forecast the M&A targets in IT industry using Support Vector Machine.” 2017. Web. 17 Nov 2019.

Vancouver:

Ke Y. Forecast the M&A targets in IT industry using Support Vector Machine. [Internet] [Thesis]. NSYSU; 2017. [cited 2019 Nov 17]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0530117-140725.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ke Y. Forecast the M&A targets in IT industry using Support Vector Machine. [Thesis]. NSYSU; 2017. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0530117-140725

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Georgia

6. Santoso, Agung. Predictors of educational attainment in Indonesia: comparing OLS regression and quantile regression approach.

Degree: MA, Educational Psychology, 2008, University of Georgia

 The current study applied quantile regression analysis to estimate the relationship between educational attainment and its predictors, and compared the results to parameter estimates using… (more)

Subjects/Keywords: quantile regression

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Santoso, A. (2008). Predictors of educational attainment in Indonesia: comparing OLS regression and quantile regression approach. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/santoso_agung_200812_ma

Chicago Manual of Style (16th Edition):

Santoso, Agung. “Predictors of educational attainment in Indonesia: comparing OLS regression and quantile regression approach.” 2008. Masters Thesis, University of Georgia. Accessed November 17, 2019. http://purl.galileo.usg.edu/uga_etd/santoso_agung_200812_ma.

MLA Handbook (7th Edition):

Santoso, Agung. “Predictors of educational attainment in Indonesia: comparing OLS regression and quantile regression approach.” 2008. Web. 17 Nov 2019.

Vancouver:

Santoso A. Predictors of educational attainment in Indonesia: comparing OLS regression and quantile regression approach. [Internet] [Masters thesis]. University of Georgia; 2008. [cited 2019 Nov 17]. Available from: http://purl.galileo.usg.edu/uga_etd/santoso_agung_200812_ma.

Council of Science Editors:

Santoso A. Predictors of educational attainment in Indonesia: comparing OLS regression and quantile regression approach. [Masters Thesis]. University of Georgia; 2008. Available from: http://purl.galileo.usg.edu/uga_etd/santoso_agung_200812_ma


University of Florida

7. Liu, Minzhao. New Approaches for Quantile Regression.

Degree: PhD, Statistics, 2014, University of Florida

Quantile regression is a powerful way to study the relationship between covariates and responses. Various approaches have been proposed to estimate quantile regression models. However,… (more)

Subjects/Keywords: Gaussian distributions; Inference; Missing data; Parametric models; Quantile regression; Quantiles; Regression analysis; Regression coefficients; Statistics; Uniform Resource Locators; quantile

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APA (6th Edition):

Liu, M. (2014). New Approaches for Quantile Regression. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0046926

Chicago Manual of Style (16th Edition):

Liu, Minzhao. “New Approaches for Quantile Regression.” 2014. Doctoral Dissertation, University of Florida. Accessed November 17, 2019. http://ufdc.ufl.edu/UFE0046926.

MLA Handbook (7th Edition):

Liu, Minzhao. “New Approaches for Quantile Regression.” 2014. Web. 17 Nov 2019.

Vancouver:

Liu M. New Approaches for Quantile Regression. [Internet] [Doctoral dissertation]. University of Florida; 2014. [cited 2019 Nov 17]. Available from: http://ufdc.ufl.edu/UFE0046926.

Council of Science Editors:

Liu M. New Approaches for Quantile Regression. [Doctoral Dissertation]. University of Florida; 2014. Available from: http://ufdc.ufl.edu/UFE0046926


Indian Institute of Science

8. Bapat, Tanuja. Sparse Multiclass And Multi-Label Classifier Design For Faster Inference.

Degree: 2011, Indian Institute of Science

 Many real-world problems like hand-written digit recognition or semantic scene classification are treated as multiclass or multi-label classification prob-lems. Solutions to these problems using support… (more)

Subjects/Keywords: Artificial Intelligence; Machine Learning; Multiclass Classification; Multi-label Classification; Sparse Max-Margin Multiclass Classifier Design; Sparse Max-Margin Classifiers; Sparse Max-Margin Multi-label Classifier Design; Support Vector Machine (SVM); Sparse Classifiers; Computer Science

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APA (6th Edition):

Bapat, T. (2011). Sparse Multiclass And Multi-Label Classifier Design For Faster Inference. (Thesis). Indian Institute of Science. Retrieved from http://hdl.handle.net/2005/2065

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):

Bapat, Tanuja. “Sparse Multiclass And Multi-Label Classifier Design For Faster Inference.” 2011. Thesis, Indian Institute of Science. Accessed November 17, 2019. http://hdl.handle.net/2005/2065.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Bapat, Tanuja. “Sparse Multiclass And Multi-Label Classifier Design For Faster Inference.” 2011. Web. 17 Nov 2019.

Vancouver:

Bapat T. Sparse Multiclass And Multi-Label Classifier Design For Faster Inference. [Internet] [Thesis]. Indian Institute of Science; 2011. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/2005/2065.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bapat T. Sparse Multiclass And Multi-Label Classifier Design For Faster Inference. [Thesis]. Indian Institute of Science; 2011. Available from: http://hdl.handle.net/2005/2065

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Indian Institute of Science

9. Bapat, Tanuja. Sparse Multiclass And Multi-Label Classifier Design For Faster Inference.

Degree: 2011, Indian Institute of Science

 Many real-world problems like hand-written digit recognition or semantic scene classification are treated as multiclass or multi-label classification prob-lems. Solutions to these problems using support… (more)

Subjects/Keywords: Artificial Intelligence; Machine Learning; Multiclass Classification; Multi-label Classification; Sparse Max-Margin Multiclass Classifier Design; Sparse Max-Margin Classifiers; Sparse Max-Margin Multi-label Classifier Design; Support Vector Machine (SVM); Sparse Classifiers; Computer Science

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Bapat, T. (2011). Sparse Multiclass And Multi-Label Classifier Design For Faster Inference. (Thesis). Indian Institute of Science. Retrieved from http://etd.iisc.ernet.in/handle/2005/2065 ; http://etd.ncsi.iisc.ernet.in/abstracts/2661/G24953-Abs.pdf

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):

Bapat, Tanuja. “Sparse Multiclass And Multi-Label Classifier Design For Faster Inference.” 2011. Thesis, Indian Institute of Science. Accessed November 17, 2019. http://etd.iisc.ernet.in/handle/2005/2065 ; http://etd.ncsi.iisc.ernet.in/abstracts/2661/G24953-Abs.pdf.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Bapat, Tanuja. “Sparse Multiclass And Multi-Label Classifier Design For Faster Inference.” 2011. Web. 17 Nov 2019.

Vancouver:

Bapat T. Sparse Multiclass And Multi-Label Classifier Design For Faster Inference. [Internet] [Thesis]. Indian Institute of Science; 2011. [cited 2019 Nov 17]. Available from: http://etd.iisc.ernet.in/handle/2005/2065 ; http://etd.ncsi.iisc.ernet.in/abstracts/2661/G24953-Abs.pdf.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bapat T. Sparse Multiclass And Multi-Label Classifier Design For Faster Inference. [Thesis]. Indian Institute of Science; 2011. Available from: http://etd.iisc.ernet.in/handle/2005/2065 ; http://etd.ncsi.iisc.ernet.in/abstracts/2661/G24953-Abs.pdf

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


AUT University

10. Ji, Xingxiu. Geometric correlation extraction method for intelligent finance data analysis .

Degree: 2012, AUT University

 Trend forecasting could be one of the most challenging things in stock market analysis, as the data associated with stock is the time series data(more)

Subjects/Keywords: Stock market prediction; SVM regression; Stock market analysis; Nonlinear prediction; Stock data correlation; Correlation SVM

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APA (6th Edition):

Ji, X. (2012). Geometric correlation extraction method for intelligent finance data analysis . (Thesis). AUT University. Retrieved from http://hdl.handle.net/10292/4749

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):

Ji, Xingxiu. “Geometric correlation extraction method for intelligent finance data analysis .” 2012. Thesis, AUT University. Accessed November 17, 2019. http://hdl.handle.net/10292/4749.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Ji, Xingxiu. “Geometric correlation extraction method for intelligent finance data analysis .” 2012. Web. 17 Nov 2019.

Vancouver:

Ji X. Geometric correlation extraction method for intelligent finance data analysis . [Internet] [Thesis]. AUT University; 2012. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/10292/4749.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ji X. Geometric correlation extraction method for intelligent finance data analysis . [Thesis]. AUT University; 2012. Available from: http://hdl.handle.net/10292/4749

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Akron

11. Shaik Abdul, Ameer Basha. SVM Classification and Analysis of Margin Distance on Microarray Data.

Degree: MS, Computer Science, 2011, University of Akron

 Support vector machine is statistical classification algorithm that classifies data by separating two classes with the help of a functional hyper plane. SVM is known… (more)

Subjects/Keywords: Bioinformatics; Computer Science; SVM; Data mining; classification; microarray; support vectors; margin distance

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APA (6th Edition):

Shaik Abdul, A. B. (2011). SVM Classification and Analysis of Margin Distance on Microarray Data. (Masters Thesis). University of Akron. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=akron1302618924

Chicago Manual of Style (16th Edition):

Shaik Abdul, Ameer Basha. “SVM Classification and Analysis of Margin Distance on Microarray Data.” 2011. Masters Thesis, University of Akron. Accessed November 17, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=akron1302618924.

MLA Handbook (7th Edition):

Shaik Abdul, Ameer Basha. “SVM Classification and Analysis of Margin Distance on Microarray Data.” 2011. Web. 17 Nov 2019.

Vancouver:

Shaik Abdul AB. SVM Classification and Analysis of Margin Distance on Microarray Data. [Internet] [Masters thesis]. University of Akron; 2011. [cited 2019 Nov 17]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=akron1302618924.

Council of Science Editors:

Shaik Abdul AB. SVM Classification and Analysis of Margin Distance on Microarray Data. [Masters Thesis]. University of Akron; 2011. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=akron1302618924


NSYSU

12. Yeh, Yen-Chen. The Influence of Tax Reduction in China`s 12th Five-Year Plan on Domestic Consumption.

Degree: Master, ICAPS, 2017, NSYSU

 Due to decrease of Chinaâs economic growth speed, Chinese government conduct a series of revolution. This paper focus on the inspection of the influence of… (more)

Subjects/Keywords: income tax; value-added tax; domestic consumption; Panel data; quantile-regression

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Yeh, Y. (2017). The Influence of Tax Reduction in China`s 12th Five-Year Plan on Domestic Consumption. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0616117-200154

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):

Yeh, Yen-Chen. “The Influence of Tax Reduction in China`s 12th Five-Year Plan on Domestic Consumption.” 2017. Thesis, NSYSU. Accessed November 17, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0616117-200154.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Yeh, Yen-Chen. “The Influence of Tax Reduction in China`s 12th Five-Year Plan on Domestic Consumption.” 2017. Web. 17 Nov 2019.

Vancouver:

Yeh Y. The Influence of Tax Reduction in China`s 12th Five-Year Plan on Domestic Consumption. [Internet] [Thesis]. NSYSU; 2017. [cited 2019 Nov 17]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0616117-200154.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yeh Y. The Influence of Tax Reduction in China`s 12th Five-Year Plan on Domestic Consumption. [Thesis]. NSYSU; 2017. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0616117-200154

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Minnesota

13. Sherwood, Benjamin Stanley. Quantile regression model selection.

Degree: PhD, Statistics, 2014, University of Minnesota

Quantile regression models the conditional quantile of a response variable. Compared to least squares, which focuses on the conditional mean, it provides a more complete… (more)

Subjects/Keywords: Missing data; Model selection; Partial linear; Quantile regression; SCAD; Semiparametric

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Sherwood, B. S. (2014). Quantile regression model selection. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/163910

Chicago Manual of Style (16th Edition):

Sherwood, Benjamin Stanley. “Quantile regression model selection.” 2014. Doctoral Dissertation, University of Minnesota. Accessed November 17, 2019. http://hdl.handle.net/11299/163910.

MLA Handbook (7th Edition):

Sherwood, Benjamin Stanley. “Quantile regression model selection.” 2014. Web. 17 Nov 2019.

Vancouver:

Sherwood BS. Quantile regression model selection. [Internet] [Doctoral dissertation]. University of Minnesota; 2014. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/11299/163910.

Council of Science Editors:

Sherwood BS. Quantile regression model selection. [Doctoral Dissertation]. University of Minnesota; 2014. Available from: http://hdl.handle.net/11299/163910


University of Texas – Austin

14. Xu, Jing, M.S. in Statistics. Predict house prices using quantile regression.

Degree: MSin Statistics, Statistics, 2018, University of Texas – Austin

Quantile Regression Model (QRM), introduced by Koenker and Bassett in 1978, is a well-established and widely used technique in theoretical and applied statistics. QRM is… (more)

Subjects/Keywords: Quantile regression; Variable selection; Bayesian Quantile Regression; Quantile regression forest

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Xu, Jing, M. S. i. S. (2018). Predict house prices using quantile regression. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/67630

Chicago Manual of Style (16th Edition):

Xu, Jing, M S in Statistics. “Predict house prices using quantile regression.” 2018. Masters Thesis, University of Texas – Austin. Accessed November 17, 2019. http://hdl.handle.net/2152/67630.

MLA Handbook (7th Edition):

Xu, Jing, M S in Statistics. “Predict house prices using quantile regression.” 2018. Web. 17 Nov 2019.

Vancouver:

Xu, Jing MSiS. Predict house prices using quantile regression. [Internet] [Masters thesis]. University of Texas – Austin; 2018. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/2152/67630.

Council of Science Editors:

Xu, Jing MSiS. Predict house prices using quantile regression. [Masters Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/67630


Brunel University

15. Bin Muhd Noor, Nik Nooruhafidzi. Statistical modelling of ECDA data for the prioritisation of defects on buried pipelines.

Degree: PhD, 2017, Brunel University

 Buried pipelines are vulnerable to the threat of corrosion. Hence, they are normally coated with a protective coating to isolate the metal substrate from the… (more)

Subjects/Keywords: Quantile regression; Bayesian quantile regression; Logistic quantile regression; Logistic regression; Pipeline coating defect size estimation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Bin Muhd Noor, N. N. (2017). Statistical modelling of ECDA data for the prioritisation of defects on buried pipelines. (Doctoral Dissertation). Brunel University. Retrieved from http://bura.brunel.ac.uk/handle/2438/16392 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.764926

Chicago Manual of Style (16th Edition):

Bin Muhd Noor, Nik Nooruhafidzi. “Statistical modelling of ECDA data for the prioritisation of defects on buried pipelines.” 2017. Doctoral Dissertation, Brunel University. Accessed November 17, 2019. http://bura.brunel.ac.uk/handle/2438/16392 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.764926.

MLA Handbook (7th Edition):

Bin Muhd Noor, Nik Nooruhafidzi. “Statistical modelling of ECDA data for the prioritisation of defects on buried pipelines.” 2017. Web. 17 Nov 2019.

Vancouver:

Bin Muhd Noor NN. Statistical modelling of ECDA data for the prioritisation of defects on buried pipelines. [Internet] [Doctoral dissertation]. Brunel University; 2017. [cited 2019 Nov 17]. Available from: http://bura.brunel.ac.uk/handle/2438/16392 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.764926.

Council of Science Editors:

Bin Muhd Noor NN. Statistical modelling of ECDA data for the prioritisation of defects on buried pipelines. [Doctoral Dissertation]. Brunel University; 2017. Available from: http://bura.brunel.ac.uk/handle/2438/16392 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.764926


University of Alberta

16. Hassan, Imran. Hierarchical Quantile Regression.

Degree: MS, Department of Mathematical and Statistical Sciences, 2014, University of Alberta

Quantile regression supplements the ordinary least squares regression and provides a complete view of a relationship between a response variable and a set of covariates.… (more)

Subjects/Keywords: quantile regression; Markov Chain Monte Carlo; asymmetric Laplace distribution; data cloning; Bayesian statistics; hierarchical models

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Hassan, I. (2014). Hierarchical Quantile Regression. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/m326m3245

Chicago Manual of Style (16th Edition):

Hassan, Imran. “Hierarchical Quantile Regression.” 2014. Masters Thesis, University of Alberta. Accessed November 17, 2019. https://era.library.ualberta.ca/files/m326m3245.

MLA Handbook (7th Edition):

Hassan, Imran. “Hierarchical Quantile Regression.” 2014. Web. 17 Nov 2019.

Vancouver:

Hassan I. Hierarchical Quantile Regression. [Internet] [Masters thesis]. University of Alberta; 2014. [cited 2019 Nov 17]. Available from: https://era.library.ualberta.ca/files/m326m3245.

Council of Science Editors:

Hassan I. Hierarchical Quantile Regression. [Masters Thesis]. University of Alberta; 2014. Available from: https://era.library.ualberta.ca/files/m326m3245


Penn State University

17. Kim, Seonjin. Three essays on nonparametric inference for longitudinal data and time series data.

Degree: PhD, Statistics, 2013, Penn State University

 This thesis consists of three essays on nonparametric inference problems for dependent data, such as longitudinal data and time series data. In the literature on… (more)

Subjects/Keywords: Locally stationary process; Longitudinal data; Measurement errors; Nonparametric inference; Quantile regression; Time series

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Kim, S. (2013). Three essays on nonparametric inference for longitudinal data and time series data. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/18557

Chicago Manual of Style (16th Edition):

Kim, Seonjin. “Three essays on nonparametric inference for longitudinal data and time series data.” 2013. Doctoral Dissertation, Penn State University. Accessed November 17, 2019. https://etda.libraries.psu.edu/catalog/18557.

MLA Handbook (7th Edition):

Kim, Seonjin. “Three essays on nonparametric inference for longitudinal data and time series data.” 2013. Web. 17 Nov 2019.

Vancouver:

Kim S. Three essays on nonparametric inference for longitudinal data and time series data. [Internet] [Doctoral dissertation]. Penn State University; 2013. [cited 2019 Nov 17]. Available from: https://etda.libraries.psu.edu/catalog/18557.

Council of Science Editors:

Kim S. Three essays on nonparametric inference for longitudinal data and time series data. [Doctoral Dissertation]. Penn State University; 2013. Available from: https://etda.libraries.psu.edu/catalog/18557


Iowa State University

18. Fostvedt, Luke Karsten. Mixed effects modeling with missing data using quantile regression and joint modeling.

Degree: 2014, Iowa State University

 This thesis focuses on many different modeling approaches that can be used to evaluate large education data sets. In education research, it is common to… (more)

Subjects/Keywords: Statistics; education data; Mixed effects modeling; multiple imputation; Quantile Regression; Education; Mathematics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Fostvedt, L. K. (2014). Mixed effects modeling with missing data using quantile regression and joint modeling. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/14153

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):

Fostvedt, Luke Karsten. “Mixed effects modeling with missing data using quantile regression and joint modeling.” 2014. Thesis, Iowa State University. Accessed November 17, 2019. https://lib.dr.iastate.edu/etd/14153.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Fostvedt, Luke Karsten. “Mixed effects modeling with missing data using quantile regression and joint modeling.” 2014. Web. 17 Nov 2019.

Vancouver:

Fostvedt LK. Mixed effects modeling with missing data using quantile regression and joint modeling. [Internet] [Thesis]. Iowa State University; 2014. [cited 2019 Nov 17]. Available from: https://lib.dr.iastate.edu/etd/14153.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Fostvedt LK. Mixed effects modeling with missing data using quantile regression and joint modeling. [Thesis]. Iowa State University; 2014. Available from: https://lib.dr.iastate.edu/etd/14153

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Southern California

19. Chang, Chih-Chieh. Bayesian multilevel quantile regression for longitudinal data.

Degree: PhD, Biostatistics, 2015, University of Southern California

 Conventional mixed effects regression focuses only on effects on the conditional mean, which may be inappropriate when the interest is in testing the effects on… (more)

Subjects/Keywords: Bayesian; multilevel modeling; mixed-effect modeling; quantile regression; longitudinal data; childhood obesity

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Chang, C. (2015). Bayesian multilevel quantile regression for longitudinal data. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/532589/rec/1043

Chicago Manual of Style (16th Edition):

Chang, Chih-Chieh. “Bayesian multilevel quantile regression for longitudinal data.” 2015. Doctoral Dissertation, University of Southern California. Accessed November 17, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/532589/rec/1043.

MLA Handbook (7th Edition):

Chang, Chih-Chieh. “Bayesian multilevel quantile regression for longitudinal data.” 2015. Web. 17 Nov 2019.

Vancouver:

Chang C. Bayesian multilevel quantile regression for longitudinal data. [Internet] [Doctoral dissertation]. University of Southern California; 2015. [cited 2019 Nov 17]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/532589/rec/1043.

Council of Science Editors:

Chang C. Bayesian multilevel quantile regression for longitudinal data. [Doctoral Dissertation]. University of Southern California; 2015. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/532589/rec/1043


Brunel University

20. Peluso, Alina. Novel regression models for discrete response.

Degree: PhD, 2017, Brunel University

 In a regression context, the aim is to analyse a response variable of interest conditional to a set of covariates. In many applications the response… (more)

Subjects/Keywords: Discrete Weibull; Count data; Difference-in-differences; Multilevel model; Parametric quantile regression model

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Peluso, A. (2017). Novel regression models for discrete response. (Doctoral Dissertation). Brunel University. Retrieved from http://bura.brunel.ac.uk/handle/2438/15581 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.764827

Chicago Manual of Style (16th Edition):

Peluso, Alina. “Novel regression models for discrete response.” 2017. Doctoral Dissertation, Brunel University. Accessed November 17, 2019. http://bura.brunel.ac.uk/handle/2438/15581 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.764827.

MLA Handbook (7th Edition):

Peluso, Alina. “Novel regression models for discrete response.” 2017. Web. 17 Nov 2019.

Vancouver:

Peluso A. Novel regression models for discrete response. [Internet] [Doctoral dissertation]. Brunel University; 2017. [cited 2019 Nov 17]. Available from: http://bura.brunel.ac.uk/handle/2438/15581 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.764827.

Council of Science Editors:

Peluso A. Novel regression models for discrete response. [Doctoral Dissertation]. Brunel University; 2017. Available from: http://bura.brunel.ac.uk/handle/2438/15581 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.764827


University of New South Wales

21. Rodrigues, Thais Carvalho Valadares. Pyramid Quantile Regression.

Degree: Mathematics & Statistics, 2017, University of New South Wales

Quantile regression models provide a wide picture of the conditional distributions of the response variable by capturing the effect of the covariates at different quantile(more)

Subjects/Keywords: Crossing quantile regression; Extremal quantile regression; Gaussian process regression; Monotonicity; Nonparametric quantile regression; O'Sullivan penalised splines; Simultaneous quantile regression; O’Sullivan penalised splines , Simultaneous quantile regression; Asymmetric Laplace distribution , Bayesian quantile pyramid , B-Splines , Crossing quantile regression; Extremal quantile regression , Gaussian process regression , Monotonicity , Nonparametric quantile regression

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Rodrigues, T. C. V. (2017). Pyramid Quantile Regression. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/58446 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:46045/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Rodrigues, Thais Carvalho Valadares. “Pyramid Quantile Regression.” 2017. Doctoral Dissertation, University of New South Wales. Accessed November 17, 2019. http://handle.unsw.edu.au/1959.4/58446 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:46045/SOURCE02?view=true.

MLA Handbook (7th Edition):

Rodrigues, Thais Carvalho Valadares. “Pyramid Quantile Regression.” 2017. Web. 17 Nov 2019.

Vancouver:

Rodrigues TCV. Pyramid Quantile Regression. [Internet] [Doctoral dissertation]. University of New South Wales; 2017. [cited 2019 Nov 17]. Available from: http://handle.unsw.edu.au/1959.4/58446 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:46045/SOURCE02?view=true.

Council of Science Editors:

Rodrigues TCV. Pyramid Quantile Regression. [Doctoral Dissertation]. University of New South Wales; 2017. Available from: http://handle.unsw.edu.au/1959.4/58446 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:46045/SOURCE02?view=true


University of Waterloo

22. Huang, Shimeng. Empirical Likelihood Quantile Regression for Right-Censored Data.

Degree: 2018, University of Waterloo

Quantile estimation of time-to-event data plays a key role in many medical applications, especially conditional on covariates of interest. In such settings, bias due to… (more)

Subjects/Keywords: Empirical likelihood; Quantile regression; EM algorithm; Length-biased data; Right-censoring; Continuity correction; Confidence intervals; Location-scale regression model

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Huang, S. (2018). Empirical Likelihood Quantile Regression for Right-Censored Data. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/14253

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):

Huang, Shimeng. “Empirical Likelihood Quantile Regression for Right-Censored Data.” 2018. Thesis, University of Waterloo. Accessed November 17, 2019. http://hdl.handle.net/10012/14253.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Huang, Shimeng. “Empirical Likelihood Quantile Regression for Right-Censored Data.” 2018. Web. 17 Nov 2019.

Vancouver:

Huang S. Empirical Likelihood Quantile Regression for Right-Censored Data. [Internet] [Thesis]. University of Waterloo; 2018. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/10012/14253.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Huang S. Empirical Likelihood Quantile Regression for Right-Censored Data. [Thesis]. University of Waterloo; 2018. Available from: http://hdl.handle.net/10012/14253

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of South Carolina

23. Wu, Junlong. Methods For Constructing Confidence Intervals For Quantile Regression Coefficients.

Degree: PhD, Epidemiology and Biostatistics, 2011, University of South Carolina

  We describe and compare methods for constructing confidence intervals for quantile regression coefficients. We consider methods based on resampling, sparsity estimation, and test-inversion. In… (more)

Subjects/Keywords: Biostatistics; Physical Sciences and Mathematics; Statistics and Probability; bisection method; confidence interval; dependent data; logistic regression; quantile regression

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wu, J. (2011). Methods For Constructing Confidence Intervals For Quantile Regression Coefficients. (Doctoral Dissertation). University of South Carolina. Retrieved from https://scholarcommons.sc.edu/etd/560

Chicago Manual of Style (16th Edition):

Wu, Junlong. “Methods For Constructing Confidence Intervals For Quantile Regression Coefficients.” 2011. Doctoral Dissertation, University of South Carolina. Accessed November 17, 2019. https://scholarcommons.sc.edu/etd/560.

MLA Handbook (7th Edition):

Wu, Junlong. “Methods For Constructing Confidence Intervals For Quantile Regression Coefficients.” 2011. Web. 17 Nov 2019.

Vancouver:

Wu J. Methods For Constructing Confidence Intervals For Quantile Regression Coefficients. [Internet] [Doctoral dissertation]. University of South Carolina; 2011. [cited 2019 Nov 17]. Available from: https://scholarcommons.sc.edu/etd/560.

Council of Science Editors:

Wu J. Methods For Constructing Confidence Intervals For Quantile Regression Coefficients. [Doctoral Dissertation]. University of South Carolina; 2011. Available from: https://scholarcommons.sc.edu/etd/560


University of Alberta

24. Shi, Qian. Variable Screening Based on Combining Quantile Regression.

Degree: MS, Department of Mathematical and Statistical Sciences, 2014, University of Alberta

 This thesis develops an efficient quantile-adaptive framework for linear and nonlinear variable screening with high-dimensional heterogeneous data. Inspired by the success of various variable screening… (more)

Subjects/Keywords: quantile regression; average quantile regression; variable screening; composite quantile regression; B-spline approximations

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Shi, Q. (2014). Variable Screening Based on Combining Quantile Regression. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/js956h083

Chicago Manual of Style (16th Edition):

Shi, Qian. “Variable Screening Based on Combining Quantile Regression.” 2014. Masters Thesis, University of Alberta. Accessed November 17, 2019. https://era.library.ualberta.ca/files/js956h083.

MLA Handbook (7th Edition):

Shi, Qian. “Variable Screening Based on Combining Quantile Regression.” 2014. Web. 17 Nov 2019.

Vancouver:

Shi Q. Variable Screening Based on Combining Quantile Regression. [Internet] [Masters thesis]. University of Alberta; 2014. [cited 2019 Nov 17]. Available from: https://era.library.ualberta.ca/files/js956h083.

Council of Science Editors:

Shi Q. Variable Screening Based on Combining Quantile Regression. [Masters Thesis]. University of Alberta; 2014. Available from: https://era.library.ualberta.ca/files/js956h083

25. Sandsveden, Daniel. Evaluation of Random Forests for Detection and Localization of Cattle Eyes.

Degree: Faculty of Science & Engineering, 2015, Linköping UniversityLinköping University

  In a time when cattle herds grow continually larger the need for automatic methods to detect diseases is ever increasing. One possible method to… (more)

Subjects/Keywords: Random Forests; HOG; LBP; SVM; Descriptor; Classifier

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APA (6th Edition):

Sandsveden, D. (2015). Evaluation of Random Forests for Detection and Localization of Cattle Eyes. (Thesis). Linköping UniversityLinköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-121540

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):

Sandsveden, Daniel. “Evaluation of Random Forests for Detection and Localization of Cattle Eyes.” 2015. Thesis, Linköping UniversityLinköping University. Accessed November 17, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-121540.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Sandsveden, Daniel. “Evaluation of Random Forests for Detection and Localization of Cattle Eyes.” 2015. Web. 17 Nov 2019.

Vancouver:

Sandsveden D. Evaluation of Random Forests for Detection and Localization of Cattle Eyes. [Internet] [Thesis]. Linköping UniversityLinköping University; 2015. [cited 2019 Nov 17]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-121540.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sandsveden D. Evaluation of Random Forests for Detection and Localization of Cattle Eyes. [Thesis]. Linköping UniversityLinköping University; 2015. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-121540

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Manchester

26. Kecojevic, Tatjana. Bootstrap inference for parametric quantile regression.

Degree: PhD, 2011, University of Manchester

 The motivation for this thesis came from the provision of a large data set from Saudi Arabia giving anthropometric measurements of children and adolescents from… (more)

Subjects/Keywords: 519; Quantile Regression; Bootstrapping

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Kecojevic, T. (2011). Bootstrap inference for parametric quantile regression. (Doctoral Dissertation). University of Manchester. Retrieved from https://www.research.manchester.ac.uk/portal/en/theses/bootstrap-inference-for-parametric-quantile-regression(194021d5-e03f-4f48-bfb8-5156819f5900).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.549093

Chicago Manual of Style (16th Edition):

Kecojevic, Tatjana. “Bootstrap inference for parametric quantile regression.” 2011. Doctoral Dissertation, University of Manchester. Accessed November 17, 2019. https://www.research.manchester.ac.uk/portal/en/theses/bootstrap-inference-for-parametric-quantile-regression(194021d5-e03f-4f48-bfb8-5156819f5900).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.549093.

MLA Handbook (7th Edition):

Kecojevic, Tatjana. “Bootstrap inference for parametric quantile regression.” 2011. Web. 17 Nov 2019.

Vancouver:

Kecojevic T. Bootstrap inference for parametric quantile regression. [Internet] [Doctoral dissertation]. University of Manchester; 2011. [cited 2019 Nov 17]. Available from: https://www.research.manchester.ac.uk/portal/en/theses/bootstrap-inference-for-parametric-quantile-regression(194021d5-e03f-4f48-bfb8-5156819f5900).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.549093.

Council of Science Editors:

Kecojevic T. Bootstrap inference for parametric quantile regression. [Doctoral Dissertation]. University of Manchester; 2011. Available from: https://www.research.manchester.ac.uk/portal/en/theses/bootstrap-inference-for-parametric-quantile-regression(194021d5-e03f-4f48-bfb8-5156819f5900).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.549093


Virginia Tech

27. Zhang, Dengfeng. Latent Class Model in Transportation Study.

Degree: PhD, Statistics, 2015, Virginia Tech

 Statistics, as a critical component in transportation research, has been widely used to analyze driver safety, travel time, traffic flow and numerous other problems. Many… (more)

Subjects/Keywords: Transportation; Mixture model; Quantile regression

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Zhang, D. (2015). Latent Class Model in Transportation Study. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/51203

Chicago Manual of Style (16th Edition):

Zhang, Dengfeng. “Latent Class Model in Transportation Study.” 2015. Doctoral Dissertation, Virginia Tech. Accessed November 17, 2019. http://hdl.handle.net/10919/51203.

MLA Handbook (7th Edition):

Zhang, Dengfeng. “Latent Class Model in Transportation Study.” 2015. Web. 17 Nov 2019.

Vancouver:

Zhang D. Latent Class Model in Transportation Study. [Internet] [Doctoral dissertation]. Virginia Tech; 2015. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/10919/51203.

Council of Science Editors:

Zhang D. Latent Class Model in Transportation Study. [Doctoral Dissertation]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/51203


Boston College

28. Jiang, Chuanliang. Three Essays In Finance Economics.

Degree: PhD, Economics, 2013, Boston College

 This dissertation contains three essays. It provides an application of quantile regression in Financial Economics. The first essay investigates whether tail dependence makes a difference… (more)

Subjects/Keywords: Financial economics; Quantile regression

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Jiang, C. (2013). Three Essays In Finance Economics. (Doctoral Dissertation). Boston College. Retrieved from http://dlib.bc.edu/islandora/object/bc-ir:101945

Chicago Manual of Style (16th Edition):

Jiang, Chuanliang. “Three Essays In Finance Economics.” 2013. Doctoral Dissertation, Boston College. Accessed November 17, 2019. http://dlib.bc.edu/islandora/object/bc-ir:101945.

MLA Handbook (7th Edition):

Jiang, Chuanliang. “Three Essays In Finance Economics.” 2013. Web. 17 Nov 2019.

Vancouver:

Jiang C. Three Essays In Finance Economics. [Internet] [Doctoral dissertation]. Boston College; 2013. [cited 2019 Nov 17]. Available from: http://dlib.bc.edu/islandora/object/bc-ir:101945.

Council of Science Editors:

Jiang C. Three Essays In Finance Economics. [Doctoral Dissertation]. Boston College; 2013. Available from: http://dlib.bc.edu/islandora/object/bc-ir:101945


Boston College

29. Sim, Nicholas. Modeling Quantile Dependence.

Degree: PhD, Economics, 2009, Boston College

 In recent years, quantile regression has achieved increasing prominence as a quantitative method of choice in applied econometric research. The methodology focuses on how the… (more)

Subjects/Keywords: Monetary Policy; Quantile Regression; Quantile-Quantile Model; Stock Returns Correlation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Sim, N. (2009). Modeling Quantile Dependence. (Doctoral Dissertation). Boston College. Retrieved from http://dlib.bc.edu/islandora/object/bc-ir:101409

Chicago Manual of Style (16th Edition):

Sim, Nicholas. “Modeling Quantile Dependence.” 2009. Doctoral Dissertation, Boston College. Accessed November 17, 2019. http://dlib.bc.edu/islandora/object/bc-ir:101409.

MLA Handbook (7th Edition):

Sim, Nicholas. “Modeling Quantile Dependence.” 2009. Web. 17 Nov 2019.

Vancouver:

Sim N. Modeling Quantile Dependence. [Internet] [Doctoral dissertation]. Boston College; 2009. [cited 2019 Nov 17]. Available from: http://dlib.bc.edu/islandora/object/bc-ir:101409.

Council of Science Editors:

Sim N. Modeling Quantile Dependence. [Doctoral Dissertation]. Boston College; 2009. Available from: http://dlib.bc.edu/islandora/object/bc-ir:101409


University of Florida

30. Kodipaka, Santhosh. A Novel Conic Section Classifier with Tractable Geometric Learning Algorithms.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2009, University of Florida

 Several pattern recognition problems in computer vision and medical diagnosis can be posed in the general framework of supervised learning. However, the high-dimensionality of the… (more)

Subjects/Keywords: Conic sections; Datasets; Discriminants; Eggshells; Epilepsy; Hyperplanes; Hyperspheres; Learning; Machine learning; Polynomials; boundaries, class, classifier, concept, conic, constraints, design, discriminant, eccentricity, evaluation, geometry, large, learning, machine, margin, nonlinear, sections, stiffness

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Kodipaka, S. (2009). A Novel Conic Section Classifier with Tractable Geometric Learning Algorithms. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0024624

Chicago Manual of Style (16th Edition):

Kodipaka, Santhosh. “A Novel Conic Section Classifier with Tractable Geometric Learning Algorithms.” 2009. Doctoral Dissertation, University of Florida. Accessed November 17, 2019. http://ufdc.ufl.edu/UFE0024624.

MLA Handbook (7th Edition):

Kodipaka, Santhosh. “A Novel Conic Section Classifier with Tractable Geometric Learning Algorithms.” 2009. Web. 17 Nov 2019.

Vancouver:

Kodipaka S. A Novel Conic Section Classifier with Tractable Geometric Learning Algorithms. [Internet] [Doctoral dissertation]. University of Florida; 2009. [cited 2019 Nov 17]. Available from: http://ufdc.ufl.edu/UFE0024624.

Council of Science Editors:

Kodipaka S. A Novel Conic Section Classifier with Tractable Geometric Learning Algorithms. [Doctoral Dissertation]. University of Florida; 2009. Available from: http://ufdc.ufl.edu/UFE0024624

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