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You searched for `subject:( Random imputation)`

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Showing records 1 – 23 of
23 total matches.

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University of Miami

1.
Tang, Fei.
* Random* Forest Missing Data Approaches.

Degree: PhD, Biostatistics (Medicine), 2017, University of Miami

URL: https://scholarlyrepository.miami.edu/oa_dissertations/1852

► *Random* forest (RF) missing data algorithms are an attractive approach for imputing missing data. They have the desirable properties of being able to handle mixed…
(more)

Subjects/Keywords: Random Forest; Imputation; MESA data; Missing data

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APA (6^{th} Edition):

Tang, F. (2017). Random Forest Missing Data Approaches. (Doctoral Dissertation). University of Miami. Retrieved from https://scholarlyrepository.miami.edu/oa_dissertations/1852

Chicago Manual of Style (16^{th} Edition):

Tang, Fei. “Random Forest Missing Data Approaches.” 2017. Doctoral Dissertation, University of Miami. Accessed December 09, 2019. https://scholarlyrepository.miami.edu/oa_dissertations/1852.

MLA Handbook (7^{th} Edition):

Tang, Fei. “Random Forest Missing Data Approaches.” 2017. Web. 09 Dec 2019.

Vancouver:

Tang F. Random Forest Missing Data Approaches. [Internet] [Doctoral dissertation]. University of Miami; 2017. [cited 2019 Dec 09]. Available from: https://scholarlyrepository.miami.edu/oa_dissertations/1852.

Council of Science Editors:

Tang F. Random Forest Missing Data Approaches. [Doctoral Dissertation]. University of Miami; 2017. Available from: https://scholarlyrepository.miami.edu/oa_dissertations/1852

Université de Montréal

2.
Nambeu, Christian O.
* Imputation* en présence de données contenant des zéros
.

Degree: 2011, Université de Montréal

URL: http://hdl.handle.net/1866/4724

► L’*imputation* simple est très souvent utilisée dans les enquêtes pour compenser pour la non-réponse partielle. Dans certaines situations, la variable nécessitant l’*imputation* prend des valeurs…
(more)

Subjects/Keywords: Imputation par la régression; Imputation aléatoire; Imputation déterministe; Imputation aléatoire équilibrée; Non-réponse partielle; Jackknife; Estimation de la Variance; Regression imputation; Random imputation; Deterministic imputation; Balanced random imputation; Item nonresponse; Jackknife; Variance estimation

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APA (6^{th} Edition):

Nambeu, C. O. (2011). Imputation en présence de données contenant des zéros . (Thesis). Université de Montréal. Retrieved from http://hdl.handle.net/1866/4724

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Nambeu, Christian O. “Imputation en présence de données contenant des zéros .” 2011. Thesis, Université de Montréal. Accessed December 09, 2019. http://hdl.handle.net/1866/4724.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Nambeu, Christian O. “Imputation en présence de données contenant des zéros .” 2011. Web. 09 Dec 2019.

Vancouver:

Nambeu CO. Imputation en présence de données contenant des zéros . [Internet] [Thesis]. Université de Montréal; 2011. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/1866/4724.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Nambeu CO. Imputation en présence de données contenant des zéros . [Thesis]. Université de Montréal; 2011. Available from: http://hdl.handle.net/1866/4724

Not specified: Masters Thesis or Doctoral Dissertation

University of Illinois – Chicago

3.
Helenowski, Irene B.
Multiple *Imputation* via a Semi-Parametric Probability Integral Transformation.

Degree: 2012, University of Illinois – Chicago

URL: http://hdl.handle.net/10027/8652

► In real data scenarios, the distribution of the data is often unknown. Therefore, methods for imputing data which relax distributional or model assumptions may be…
(more)

Subjects/Keywords: multiple imputation; random number generation; empirical cumulative distribution function

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APA (6^{th} Edition):

Helenowski, I. B. (2012). Multiple Imputation via a Semi-Parametric Probability Integral Transformation. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/8652

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Helenowski, Irene B. “Multiple Imputation via a Semi-Parametric Probability Integral Transformation.” 2012. Thesis, University of Illinois – Chicago. Accessed December 09, 2019. http://hdl.handle.net/10027/8652.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Helenowski, Irene B. “Multiple Imputation via a Semi-Parametric Probability Integral Transformation.” 2012. Web. 09 Dec 2019.

Vancouver:

Helenowski IB. Multiple Imputation via a Semi-Parametric Probability Integral Transformation. [Internet] [Thesis]. University of Illinois – Chicago; 2012. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/10027/8652.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Helenowski IB. Multiple Imputation via a Semi-Parametric Probability Integral Transformation. [Thesis]. University of Illinois – Chicago; 2012. Available from: http://hdl.handle.net/10027/8652

Not specified: Masters Thesis or Doctoral Dissertation

Boise State University

4. Dhakal, Shital. Assessing the Limitations and Capabilities of Lidar and Landsat 8 to Estimate the Aboveground Vegetation Biomass and Cover in a Rangeland Ecosystem Using a Machine Learning Algorithm.

Degree: 2016, Boise State University

URL: https://scholarworks.boisestate.edu/td/1108

► Remote sensing based quantification of semiarid rangeland vegetation provides the large scale observations required for monitoring native plant distribution, estimating fuel loads, modeling climate and…
(more)

Subjects/Keywords: remote sensing; lidar; landsat; biomass; random forest; imputation; Biology; Engineering

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APA (6^{th} Edition):

Dhakal, S. (2016). Assessing the Limitations and Capabilities of Lidar and Landsat 8 to Estimate the Aboveground Vegetation Biomass and Cover in a Rangeland Ecosystem Using a Machine Learning Algorithm. (Thesis). Boise State University. Retrieved from https://scholarworks.boisestate.edu/td/1108

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Dhakal, Shital. “Assessing the Limitations and Capabilities of Lidar and Landsat 8 to Estimate the Aboveground Vegetation Biomass and Cover in a Rangeland Ecosystem Using a Machine Learning Algorithm.” 2016. Thesis, Boise State University. Accessed December 09, 2019. https://scholarworks.boisestate.edu/td/1108.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Dhakal, Shital. “Assessing the Limitations and Capabilities of Lidar and Landsat 8 to Estimate the Aboveground Vegetation Biomass and Cover in a Rangeland Ecosystem Using a Machine Learning Algorithm.” 2016. Web. 09 Dec 2019.

Vancouver:

Dhakal S. Assessing the Limitations and Capabilities of Lidar and Landsat 8 to Estimate the Aboveground Vegetation Biomass and Cover in a Rangeland Ecosystem Using a Machine Learning Algorithm. [Internet] [Thesis]. Boise State University; 2016. [cited 2019 Dec 09]. Available from: https://scholarworks.boisestate.edu/td/1108.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Dhakal S. Assessing the Limitations and Capabilities of Lidar and Landsat 8 to Estimate the Aboveground Vegetation Biomass and Cover in a Rangeland Ecosystem Using a Machine Learning Algorithm. [Thesis]. Boise State University; 2016. Available from: https://scholarworks.boisestate.edu/td/1108

Not specified: Masters Thesis or Doctoral Dissertation

University of Adelaide

5.
Sullivan, Thomas Richard.
Multiple *Imputation* for Handling Missing Outcome Data.

Degree: 2017, University of Adelaide

URL: http://hdl.handle.net/2440/119248

► Background: Multiple *imputation* is a widely used approach to handling missing data. Despite a growing evidence base for its use, implementation in practical settings remains…
(more)

Subjects/Keywords: Multiple imputation; missing data; missing at random; clinical trials

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

APA (6^{th} Edition):

Sullivan, T. R. (2017). Multiple Imputation for Handling Missing Outcome Data. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/119248

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Sullivan, Thomas Richard. “Multiple Imputation for Handling Missing Outcome Data.” 2017. Thesis, University of Adelaide. Accessed December 09, 2019. http://hdl.handle.net/2440/119248.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Sullivan, Thomas Richard. “Multiple Imputation for Handling Missing Outcome Data.” 2017. Web. 09 Dec 2019.

Vancouver:

Sullivan TR. Multiple Imputation for Handling Missing Outcome Data. [Internet] [Thesis]. University of Adelaide; 2017. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/2440/119248.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sullivan TR. Multiple Imputation for Handling Missing Outcome Data. [Thesis]. University of Adelaide; 2017. Available from: http://hdl.handle.net/2440/119248

Not specified: Masters Thesis or Doctoral Dissertation

East Tennessee State University

6.
Oketch, Tobias O.
Performance of *Imputation* Algorithms on Artificially Produced Missing at *Random* Data.

Degree: MS, Mathematical Sciences, 2017, East Tennessee State University

URL: https://dc.etsu.edu/etd/3217

► Missing data is one of the challenges we are facing today in modeling valid statistical models. It reduces the representativeness of the data samples.…
(more)

Subjects/Keywords: Missing not at random; Missing completely at random; Missing at random; Multiple imputation; Multiple imputation by chained equation; Relative efficiency.; Applied Statistics; Multivariate Analysis; Statistical Models

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

APA (6^{th} Edition):

Oketch, T. O. (2017). Performance of Imputation Algorithms on Artificially Produced Missing at Random Data. (Masters Thesis). East Tennessee State University. Retrieved from https://dc.etsu.edu/etd/3217

Chicago Manual of Style (16^{th} Edition):

Oketch, Tobias O. “Performance of Imputation Algorithms on Artificially Produced Missing at Random Data.” 2017. Masters Thesis, East Tennessee State University. Accessed December 09, 2019. https://dc.etsu.edu/etd/3217.

MLA Handbook (7^{th} Edition):

Oketch, Tobias O. “Performance of Imputation Algorithms on Artificially Produced Missing at Random Data.” 2017. Web. 09 Dec 2019.

Vancouver:

Oketch TO. Performance of Imputation Algorithms on Artificially Produced Missing at Random Data. [Internet] [Masters thesis]. East Tennessee State University; 2017. [cited 2019 Dec 09]. Available from: https://dc.etsu.edu/etd/3217.

Council of Science Editors:

Oketch TO. Performance of Imputation Algorithms on Artificially Produced Missing at Random Data. [Masters Thesis]. East Tennessee State University; 2017. Available from: https://dc.etsu.edu/etd/3217

Georgia State University

7.
Alemdar, Meltem.
A Monte Carlo Study: The Impact of Missing Data in Cross-Classification *Random* Effects Models.

Degree: PhD, Educational Policy Studies, 2009, Georgia State University

URL: https://scholarworks.gsu.edu/eps_diss/34

► Unlike multilevel data with a purely nested structure, data that are cross-classified not only may be clustered into hierarchically ordered units but also may belong…
(more)

Subjects/Keywords: Cross- Classified Data; Cross-Classified Random Effects Models; Missing Data; Multiple Imputation; Education; Education Policy

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APA (6^{th} Edition):

Alemdar, M. (2009). A Monte Carlo Study: The Impact of Missing Data in Cross-Classification Random Effects Models. (Doctoral Dissertation). Georgia State University. Retrieved from https://scholarworks.gsu.edu/eps_diss/34

Chicago Manual of Style (16^{th} Edition):

Alemdar, Meltem. “A Monte Carlo Study: The Impact of Missing Data in Cross-Classification Random Effects Models.” 2009. Doctoral Dissertation, Georgia State University. Accessed December 09, 2019. https://scholarworks.gsu.edu/eps_diss/34.

MLA Handbook (7^{th} Edition):

Alemdar, Meltem. “A Monte Carlo Study: The Impact of Missing Data in Cross-Classification Random Effects Models.” 2009. Web. 09 Dec 2019.

Vancouver:

Alemdar M. A Monte Carlo Study: The Impact of Missing Data in Cross-Classification Random Effects Models. [Internet] [Doctoral dissertation]. Georgia State University; 2009. [cited 2019 Dec 09]. Available from: https://scholarworks.gsu.edu/eps_diss/34.

Council of Science Editors:

Alemdar M. A Monte Carlo Study: The Impact of Missing Data in Cross-Classification Random Effects Models. [Doctoral Dissertation]. Georgia State University; 2009. Available from: https://scholarworks.gsu.edu/eps_diss/34

Virginia Commonwealth University

8.
Zheng, Xiyu.
SENSITIVITY ANALYSIS IN HANDLING DISCRETE DATA MISSING AT *RANDOM* IN HIERARCHICAL LINEAR MODELS VIA MULTIVARIATE NORMALITY.

Degree: MS, Biostatistics, 2016, Virginia Commonwealth University

URL: https://scholarscompass.vcu.edu/etd/4403

► Abstract In a two-level hierarchical linear model(HLM2), the outcome as well as covariates may have missing values at any of the levels. One way…
(more)

Subjects/Keywords: Missing at Random; Maximum Likelihood; Multiple Imputation; Hierarchical Linear Model; Social Statistics

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

APA (6^{th} Edition):

Zheng, X. (2016). SENSITIVITY ANALYSIS IN HANDLING DISCRETE DATA MISSING AT RANDOM IN HIERARCHICAL LINEAR MODELS VIA MULTIVARIATE NORMALITY. (Thesis). Virginia Commonwealth University. Retrieved from https://scholarscompass.vcu.edu/etd/4403

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Zheng, Xiyu. “SENSITIVITY ANALYSIS IN HANDLING DISCRETE DATA MISSING AT RANDOM IN HIERARCHICAL LINEAR MODELS VIA MULTIVARIATE NORMALITY.” 2016. Thesis, Virginia Commonwealth University. Accessed December 09, 2019. https://scholarscompass.vcu.edu/etd/4403.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Zheng, Xiyu. “SENSITIVITY ANALYSIS IN HANDLING DISCRETE DATA MISSING AT RANDOM IN HIERARCHICAL LINEAR MODELS VIA MULTIVARIATE NORMALITY.” 2016. Web. 09 Dec 2019.

Vancouver:

Zheng X. SENSITIVITY ANALYSIS IN HANDLING DISCRETE DATA MISSING AT RANDOM IN HIERARCHICAL LINEAR MODELS VIA MULTIVARIATE NORMALITY. [Internet] [Thesis]. Virginia Commonwealth University; 2016. [cited 2019 Dec 09]. Available from: https://scholarscompass.vcu.edu/etd/4403.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zheng X. SENSITIVITY ANALYSIS IN HANDLING DISCRETE DATA MISSING AT RANDOM IN HIERARCHICAL LINEAR MODELS VIA MULTIVARIATE NORMALITY. [Thesis]. Virginia Commonwealth University; 2016. Available from: https://scholarscompass.vcu.edu/etd/4403

Not specified: Masters Thesis or Doctoral Dissertation

Universitat Politècnica de València

9. Jiménez Montero, José Antonio. Selección genómica en poblaciones reducidas de vacuno de leche.

Degree: 2013, Universitat Politècnica de València

URL: http://hdl.handle.net/10251/27649

► La selección genómica está cambiando profundamente el mercado del vacuno de leche. En la actualidad, es posible obtener una alta precisión en las valoraciones genéticas…
(more)

Subjects/Keywords: Genomic Selection; Predictive Ability; Random boosting; Genotyping strategies; Spanish population; Dairy cattle; Imputation

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APA (6^{th} Edition):

Jiménez Montero, J. A. (2013). Selección genómica en poblaciones reducidas de vacuno de leche. (Doctoral Dissertation). Universitat Politècnica de València. Retrieved from http://hdl.handle.net/10251/27649

Chicago Manual of Style (16^{th} Edition):

Jiménez Montero, José Antonio. “Selección genómica en poblaciones reducidas de vacuno de leche. ” 2013. Doctoral Dissertation, Universitat Politècnica de València. Accessed December 09, 2019. http://hdl.handle.net/10251/27649.

MLA Handbook (7^{th} Edition):

Jiménez Montero, José Antonio. “Selección genómica en poblaciones reducidas de vacuno de leche. ” 2013. Web. 09 Dec 2019.

Vancouver:

Jiménez Montero JA. Selección genómica en poblaciones reducidas de vacuno de leche. [Internet] [Doctoral dissertation]. Universitat Politècnica de València; 2013. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/10251/27649.

Council of Science Editors:

Jiménez Montero JA. Selección genómica en poblaciones reducidas de vacuno de leche. [Doctoral Dissertation]. Universitat Politècnica de València; 2013. Available from: http://hdl.handle.net/10251/27649

University of Guelph

10. Lazure, Adam. Improving Credit Classification Using Machine Learning Techniques .

Degree: 2017, University of Guelph

URL: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/11962

► The quantification of credit risk is an ever expanding topic of discussion in the field of finance. In order to prevent economic loss, risk management…
(more)

Subjects/Keywords: credit risk; multiple imputation by chained equations; support vector machine; random forest; predictive mean matching

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APA (6^{th} Edition):

Lazure, A. (2017). Improving Credit Classification Using Machine Learning Techniques . (Thesis). University of Guelph. Retrieved from https://atrium.lib.uoguelph.ca/xmlui/handle/10214/11962

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Lazure, Adam. “Improving Credit Classification Using Machine Learning Techniques .” 2017. Thesis, University of Guelph. Accessed December 09, 2019. https://atrium.lib.uoguelph.ca/xmlui/handle/10214/11962.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Lazure, Adam. “Improving Credit Classification Using Machine Learning Techniques .” 2017. Web. 09 Dec 2019.

Vancouver:

Lazure A. Improving Credit Classification Using Machine Learning Techniques . [Internet] [Thesis]. University of Guelph; 2017. [cited 2019 Dec 09]. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/11962.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lazure A. Improving Credit Classification Using Machine Learning Techniques . [Thesis]. University of Guelph; 2017. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/11962

Not specified: Masters Thesis or Doctoral Dissertation

11. Crawford, Scott. Efficient Estimation in a Regression Model with Missing Responses.

Degree: 2012, Texas A&M University

URL: http://hdl.handle.net/1969.1/ETD-TAMU-2012-08-11396

► This article examines methods to efficiently estimate the mean response in a linear model with an unknown error distribution under the assumption that the responses…
(more)

Subjects/Keywords: Efficiency; Missing at Random; Regression; Full Imputation

…a semiparametric model under the assumption that the responses are missing at *random*. A… …*imputation*, i.e. without estimation of the missing
values.
Schick (1993) explains how… …the assumption of data missing at
*random*. M¨
uller et al. (2006) propose the… …method of full *imputation*, which estimates
all the responses, as an improvement over partial… …*imputation*, where only the missing
responses are imputed. M¨
uller (2009) showed that in…

Record Details Similar Records

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

APA (6^{th} Edition):

Crawford, S. (2012). Efficient Estimation in a Regression Model with Missing Responses. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2012-08-11396

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Crawford, Scott. “Efficient Estimation in a Regression Model with Missing Responses.” 2012. Thesis, Texas A&M University. Accessed December 09, 2019. http://hdl.handle.net/1969.1/ETD-TAMU-2012-08-11396.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Crawford, Scott. “Efficient Estimation in a Regression Model with Missing Responses.” 2012. Web. 09 Dec 2019.

Vancouver:

Crawford S. Efficient Estimation in a Regression Model with Missing Responses. [Internet] [Thesis]. Texas A&M University; 2012. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2012-08-11396.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Crawford S. Efficient Estimation in a Regression Model with Missing Responses. [Thesis]. Texas A&M University; 2012. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2012-08-11396

Not specified: Masters Thesis or Doctoral Dissertation

12.
Yılmaz, Hülya.
* Random* Forests yönteminde kayıp veri probleminin incelenmesi ve sağlık alanında bir uygulama
.

Degree: Biyoistatistik, 2014, Eskisehir Osmangazi University

URL: http://hdl.handle.net/11684/249

► Bu tez çalışmasında, kayıp verili sınıflandırma probleminde kullanılan *Random* Forests (RF) yönteminin kayıp değer atama algoritmasıyla, K En Yakın Komşu (KNN) ile kayıp değer atama…
(more)

Subjects/Keywords: Random Forests; Kayıp Veri Analizi; K En Yakın Komşu ile Kayıp Değer Atama Yöntemi; Missing Data Analysis; KNN Imputation Method

Record Details Similar Records

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

APA (6^{th} Edition):

Yılmaz, H. (2014). Random Forests yönteminde kayıp veri probleminin incelenmesi ve sağlık alanında bir uygulama . (Thesis). Eskisehir Osmangazi University. Retrieved from http://hdl.handle.net/11684/249

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Yılmaz, Hülya. “Random Forests yönteminde kayıp veri probleminin incelenmesi ve sağlık alanında bir uygulama .” 2014. Thesis, Eskisehir Osmangazi University. Accessed December 09, 2019. http://hdl.handle.net/11684/249.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Yılmaz, Hülya. “Random Forests yönteminde kayıp veri probleminin incelenmesi ve sağlık alanında bir uygulama .” 2014. Web. 09 Dec 2019.

Vancouver:

Yılmaz H. Random Forests yönteminde kayıp veri probleminin incelenmesi ve sağlık alanında bir uygulama . [Internet] [Thesis]. Eskisehir Osmangazi University; 2014. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/11684/249.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yılmaz H. Random Forests yönteminde kayıp veri probleminin incelenmesi ve sağlık alanında bir uygulama . [Thesis]. Eskisehir Osmangazi University; 2014. Available from: http://hdl.handle.net/11684/249

Not specified: Masters Thesis or Doctoral Dissertation

Harvard University

13. Liublinska, Viktoriia. Sensitivity Analyses in Empirical Studies Plagued with Missing Data.

Degree: PhD, Statistics, 2013, Harvard University

URL: http://nrs.harvard.edu/urn-3:HUL.InstRepos:11124841

►

Analyses of data with missing values often require assumptions about missingness mechanisms that cannot be assessed empirically, highlighting the need for sensitivity analyses. However, universal… (more)

Subjects/Keywords: Statistics; clinical trial; graphical sensitivity analysis; missing not at random; multiple imputation; principle stratification; tipping-point analysis

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

APA (6^{th} Edition):

Liublinska, V. (2013). Sensitivity Analyses in Empirical Studies Plagued with Missing Data. (Doctoral Dissertation). Harvard University. Retrieved from http://nrs.harvard.edu/urn-3:HUL.InstRepos:11124841

Chicago Manual of Style (16^{th} Edition):

Liublinska, Viktoriia. “Sensitivity Analyses in Empirical Studies Plagued with Missing Data.” 2013. Doctoral Dissertation, Harvard University. Accessed December 09, 2019. http://nrs.harvard.edu/urn-3:HUL.InstRepos:11124841.

MLA Handbook (7^{th} Edition):

Liublinska, Viktoriia. “Sensitivity Analyses in Empirical Studies Plagued with Missing Data.” 2013. Web. 09 Dec 2019.

Vancouver:

Liublinska V. Sensitivity Analyses in Empirical Studies Plagued with Missing Data. [Internet] [Doctoral dissertation]. Harvard University; 2013. [cited 2019 Dec 09]. Available from: http://nrs.harvard.edu/urn-3:HUL.InstRepos:11124841.

Council of Science Editors:

Liublinska V. Sensitivity Analyses in Empirical Studies Plagued with Missing Data. [Doctoral Dissertation]. Harvard University; 2013. Available from: http://nrs.harvard.edu/urn-3:HUL.InstRepos:11124841

University of Newcastle

14. Bowe, Steven J. Longitudinal data analysis: statistical methods for analysing longitudinal changes in health related quality of life which account for deaths and impute for longitudinal missing data.

Degree: PhD, 2010, University of Newcastle

URL: http://hdl.handle.net/1959.13/808716

►

Research Doctorate - Doctor of Philosophy (PhD)

Analysis of data from longitudinal studies is made more complex by the death of study participants over time.… (more)

Subjects/Keywords: deaths; SF-36; longitudinal studies; multiple imputation; elderly populations; health related quality of life; missing data; missing not at random

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

APA (6^{th} Edition):

Bowe, S. J. (2010). Longitudinal data analysis: statistical methods for analysing longitudinal changes in health related quality of life which account for deaths and impute for longitudinal missing data. (Doctoral Dissertation). University of Newcastle. Retrieved from http://hdl.handle.net/1959.13/808716

Chicago Manual of Style (16^{th} Edition):

Bowe, Steven J. “Longitudinal data analysis: statistical methods for analysing longitudinal changes in health related quality of life which account for deaths and impute for longitudinal missing data.” 2010. Doctoral Dissertation, University of Newcastle. Accessed December 09, 2019. http://hdl.handle.net/1959.13/808716.

MLA Handbook (7^{th} Edition):

Bowe, Steven J. “Longitudinal data analysis: statistical methods for analysing longitudinal changes in health related quality of life which account for deaths and impute for longitudinal missing data.” 2010. Web. 09 Dec 2019.

Vancouver:

Bowe SJ. Longitudinal data analysis: statistical methods for analysing longitudinal changes in health related quality of life which account for deaths and impute for longitudinal missing data. [Internet] [Doctoral dissertation]. University of Newcastle; 2010. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/1959.13/808716.

Council of Science Editors:

Bowe SJ. Longitudinal data analysis: statistical methods for analysing longitudinal changes in health related quality of life which account for deaths and impute for longitudinal missing data. [Doctoral Dissertation]. University of Newcastle; 2010. Available from: http://hdl.handle.net/1959.13/808716

Uppsala University

15. Säfström, Stella. Predicting the Unobserved : A statistical analysis of missing data techniques for binary classification.

Degree: Statistics, 2019, Uppsala University

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-388581

► The aim of the thesis is to investigate how the classification performance of *random* forest and logistic regression differ, given an imbalanced data set…
(more)

Subjects/Keywords: Random forest; logistic regression; imputation; classification; MCAR; missing data; imbalanced data; Probability Theory and Statistics; Sannolikhetsteori och statistik

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

APA (6^{th} Edition):

Säfström, S. (2019). Predicting the Unobserved : A statistical analysis of missing data techniques for binary classification. (Thesis). Uppsala University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-388581

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Säfström, Stella. “Predicting the Unobserved : A statistical analysis of missing data techniques for binary classification.” 2019. Thesis, Uppsala University. Accessed December 09, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-388581.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Säfström, Stella. “Predicting the Unobserved : A statistical analysis of missing data techniques for binary classification.” 2019. Web. 09 Dec 2019.

Vancouver:

Säfström S. Predicting the Unobserved : A statistical analysis of missing data techniques for binary classification. [Internet] [Thesis]. Uppsala University; 2019. [cited 2019 Dec 09]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-388581.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Säfström S. Predicting the Unobserved : A statistical analysis of missing data techniques for binary classification. [Thesis]. Uppsala University; 2019. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-388581

Not specified: Masters Thesis or Doctoral Dissertation

University of KwaZulu-Natal

16. [No author]. The impact of missing data on clinical trials : a re-analysis of a placebo controlled trial of Hypericum perforatum (St Johns wort) and sertraline in major depressive disorder.

Degree: 2014, University of KwaZulu-Natal

URL: http://dx.doi.org/10.1007/s00213-013-3344-x

► Rationale and objective Hypericum perforatum (St John's wort) is used to treat depression, but the effectiveness has not been established. Recent guidelines described the analysis…
(more)

Subjects/Keywords: St John's wort.; Hypericum perforatum.; Herbal medicine.; Antidepressant.; Sertraline.; Hamilton depression scale.; Bayesian.; Multiple imputation.; Missing at random.; Missing not at random.

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APA (6^{th} Edition):

author], [. (2014). The impact of missing data on clinical trials : a re-analysis of a placebo controlled trial of Hypericum perforatum (St Johns wort) and sertraline in major depressive disorder. (Thesis). University of KwaZulu-Natal. Retrieved from http://dx.doi.org/10.1007/s00213-013-3344-x

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

author], [No. “The impact of missing data on clinical trials : a re-analysis of a placebo controlled trial of Hypericum perforatum (St Johns wort) and sertraline in major depressive disorder. ” 2014. Thesis, University of KwaZulu-Natal. Accessed December 09, 2019. http://dx.doi.org/10.1007/s00213-013-3344-x.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

author], [No. “The impact of missing data on clinical trials : a re-analysis of a placebo controlled trial of Hypericum perforatum (St Johns wort) and sertraline in major depressive disorder. ” 2014. Web. 09 Dec 2019.

Vancouver:

author] [. The impact of missing data on clinical trials : a re-analysis of a placebo controlled trial of Hypericum perforatum (St Johns wort) and sertraline in major depressive disorder. [Internet] [Thesis]. University of KwaZulu-Natal; 2014. [cited 2019 Dec 09]. Available from: http://dx.doi.org/10.1007/s00213-013-3344-x.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

author] [. The impact of missing data on clinical trials : a re-analysis of a placebo controlled trial of Hypericum perforatum (St Johns wort) and sertraline in major depressive disorder. [Thesis]. University of KwaZulu-Natal; 2014. Available from: http://dx.doi.org/10.1007/s00213-013-3344-x

Not specified: Masters Thesis or Doctoral Dissertation

17. Hassan, Mahmoud. Fiscalité environnementale, dette publique et croissance économique : une analyse macroéconomique : Environmental taxation, public debt and economic growth : a macroeconomic analysis.

Degree: Docteur es, Sciences économiques, 2018, Angers

URL: http://www.theses.fr/2018ANGE0015

►

Les politiques environnementales, notamment celles recourant aux instruments fiscaux, ont pris une place de plus en plus importante dans un grand nombre de pays durant… (more)

Subjects/Keywords: Fiscalité environnementale; Dette publique; Croissance économique; Imputation multiple; Effets aléatoires corrélés; Modèle à équations simultanées; Environmental taxation; Public debt; Economic growth; Multiple imputation; Correlated random effects; Simultaneous equations model; 338.9; 336.34; 333.7

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

APA (6^{th} Edition):

Hassan, M. (2018). Fiscalité environnementale, dette publique et croissance économique : une analyse macroéconomique : Environmental taxation, public debt and economic growth : a macroeconomic analysis. (Doctoral Dissertation). Angers. Retrieved from http://www.theses.fr/2018ANGE0015

Chicago Manual of Style (16^{th} Edition):

Hassan, Mahmoud. “Fiscalité environnementale, dette publique et croissance économique : une analyse macroéconomique : Environmental taxation, public debt and economic growth : a macroeconomic analysis.” 2018. Doctoral Dissertation, Angers. Accessed December 09, 2019. http://www.theses.fr/2018ANGE0015.

MLA Handbook (7^{th} Edition):

Hassan, Mahmoud. “Fiscalité environnementale, dette publique et croissance économique : une analyse macroéconomique : Environmental taxation, public debt and economic growth : a macroeconomic analysis.” 2018. Web. 09 Dec 2019.

Vancouver:

Hassan M. Fiscalité environnementale, dette publique et croissance économique : une analyse macroéconomique : Environmental taxation, public debt and economic growth : a macroeconomic analysis. [Internet] [Doctoral dissertation]. Angers; 2018. [cited 2019 Dec 09]. Available from: http://www.theses.fr/2018ANGE0015.

Council of Science Editors:

Hassan M. Fiscalité environnementale, dette publique et croissance économique : une analyse macroéconomique : Environmental taxation, public debt and economic growth : a macroeconomic analysis. [Doctoral Dissertation]. Angers; 2018. Available from: http://www.theses.fr/2018ANGE0015

Kent State University

18. Giovannone, Carrie Lynn. A Longitudinal Study of School Practices and Students’ Characteristics that Influence Students' Mathematics and Reading Performance of Arizona Charter Middle Schools.

Degree: PhD, College and Graduate School of Education, Health and Human Services / School of Foundations, Leadership and Administration, 2010, Kent State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=kent1288808181

► In 1995, Arizona legislators passed laws specifically to implement charter schools in Arizona. Approving 15 year charters (i.e., contracts), allowing charter schools to cross…
(more)

Subjects/Keywords: Education; HLM; CCREM; hierarchical linear modeling; multi-level modeling; cross-classified random effects; charter schools; teacher experience; class size; multiple imputation; mathematics achievement; reading achievement; school choice

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

APA (6^{th} Edition):

Giovannone, C. L. (2010). A Longitudinal Study of School Practices and Students’ Characteristics that Influence Students' Mathematics and Reading Performance of Arizona Charter Middle Schools. (Doctoral Dissertation). Kent State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=kent1288808181

Chicago Manual of Style (16^{th} Edition):

Giovannone, Carrie Lynn. “A Longitudinal Study of School Practices and Students’ Characteristics that Influence Students' Mathematics and Reading Performance of Arizona Charter Middle Schools.” 2010. Doctoral Dissertation, Kent State University. Accessed December 09, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=kent1288808181.

MLA Handbook (7^{th} Edition):

Giovannone, Carrie Lynn. “A Longitudinal Study of School Practices and Students’ Characteristics that Influence Students' Mathematics and Reading Performance of Arizona Charter Middle Schools.” 2010. Web. 09 Dec 2019.

Vancouver:

Giovannone CL. A Longitudinal Study of School Practices and Students’ Characteristics that Influence Students' Mathematics and Reading Performance of Arizona Charter Middle Schools. [Internet] [Doctoral dissertation]. Kent State University; 2010. [cited 2019 Dec 09]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=kent1288808181.

Council of Science Editors:

Giovannone CL. A Longitudinal Study of School Practices and Students’ Characteristics that Influence Students' Mathematics and Reading Performance of Arizona Charter Middle Schools. [Doctoral Dissertation]. Kent State University; 2010. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=kent1288808181

19.
Pan, Wensi.
Comparison of *Imputation* Methods on Estimating Regression Equation in MNAR Mechanism.

Degree: Statistics, 2012, Uppsala University

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-175772

► In this article, we propose an overview of missing data problem, introduce three missing data mechanisms and study general solutions to them when estimating…
(more)

Subjects/Keywords: Missing not at Random; Listwise Deletion; Multiple Imputation

…Such a method is multiple *imputation*.
Missing not at *random* (MNAR) indicates that… …*imputation* and maximum likelihood need an assumption
of missing at *random* or missing completely at… …that simple *imputation* methods were inadequate for missing
not at *random* (MNAR)… …*random* (MCAR)
P( R Z , ) P ( R )
B. Missing at *random*… …x28;MAR)
P( R Z , ) P( R Zobs , )
C. Missing not at *random*…

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

APA (6^{th} Edition):

Pan, W. (2012). Comparison of Imputation Methods on Estimating Regression Equation in MNAR Mechanism. (Thesis). Uppsala University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-175772

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Pan, Wensi. “Comparison of Imputation Methods on Estimating Regression Equation in MNAR Mechanism.” 2012. Thesis, Uppsala University. Accessed December 09, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-175772.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Pan, Wensi. “Comparison of Imputation Methods on Estimating Regression Equation in MNAR Mechanism.” 2012. Web. 09 Dec 2019.

Vancouver:

Pan W. Comparison of Imputation Methods on Estimating Regression Equation in MNAR Mechanism. [Internet] [Thesis]. Uppsala University; 2012. [cited 2019 Dec 09]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-175772.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Pan W. Comparison of Imputation Methods on Estimating Regression Equation in MNAR Mechanism. [Thesis]. Uppsala University; 2012. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-175772

Not specified: Masters Thesis or Doctoral Dissertation

Arizona State University

20.
Kunze, Katie Lynn.
Multiple *Imputation* for Two-Level Hierarchical Models with
Categorical Variables and Missing at *Random* Data.

Degree: Educational Psychology, 2016, Arizona State University

URL: http://repository.asu.edu/items/40705

► Accurate data analysis and interpretation of results may be influenced by many potential factors. The factors of interest in the current work are the chosen…
(more)

Subjects/Keywords: Quantitative psychology; Statistics; Educational tests &; measurements; Bayesian Estimation; Categorical Data Analysis; Missing at Random Data; Missing Data Theory; Multilevel Modeling; Multiple Imputation

Record Details Similar Records

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

APA (6^{th} Edition):

Kunze, K. L. (2016). Multiple Imputation for Two-Level Hierarchical Models with Categorical Variables and Missing at Random Data. (Doctoral Dissertation). Arizona State University. Retrieved from http://repository.asu.edu/items/40705

Chicago Manual of Style (16^{th} Edition):

Kunze, Katie Lynn. “Multiple Imputation for Two-Level Hierarchical Models with Categorical Variables and Missing at Random Data.” 2016. Doctoral Dissertation, Arizona State University. Accessed December 09, 2019. http://repository.asu.edu/items/40705.

MLA Handbook (7^{th} Edition):

Kunze, Katie Lynn. “Multiple Imputation for Two-Level Hierarchical Models with Categorical Variables and Missing at Random Data.” 2016. Web. 09 Dec 2019.

Vancouver:

Kunze KL. Multiple Imputation for Two-Level Hierarchical Models with Categorical Variables and Missing at Random Data. [Internet] [Doctoral dissertation]. Arizona State University; 2016. [cited 2019 Dec 09]. Available from: http://repository.asu.edu/items/40705.

Council of Science Editors:

Kunze KL. Multiple Imputation for Two-Level Hierarchical Models with Categorical Variables and Missing at Random Data. [Doctoral Dissertation]. Arizona State University; 2016. Available from: http://repository.asu.edu/items/40705

North Carolina State University

21. Gao, Guozhi. Semiparametric Estimators for the Regression Coefficients in the Linear Transformation Competing Risks Models with Missing Cause of Failure.

Degree: PhD, Statistics, 2006, North Carolina State University

URL: http://www.lib.ncsu.edu/resolver/1840.16/5228

► In many clinical studies, researchers are mainly interested in studying the effects of some prognostic factors on the hazard of failure from a specific cause…
(more)

Subjects/Keywords: Influence function; Multiple Imputation; Missing at random; Semiparametric estimator; Inverse probability weighted; Linear transformation model; Double Robustness; Competing risks; Cause-specific hazard

Record Details Similar Records

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

APA (6^{th} Edition):

Gao, G. (2006). Semiparametric Estimators for the Regression Coefficients in the Linear Transformation Competing Risks Models with Missing Cause of Failure. (Doctoral Dissertation). North Carolina State University. Retrieved from http://www.lib.ncsu.edu/resolver/1840.16/5228

Chicago Manual of Style (16^{th} Edition):

Gao, Guozhi. “Semiparametric Estimators for the Regression Coefficients in the Linear Transformation Competing Risks Models with Missing Cause of Failure.” 2006. Doctoral Dissertation, North Carolina State University. Accessed December 09, 2019. http://www.lib.ncsu.edu/resolver/1840.16/5228.

MLA Handbook (7^{th} Edition):

Gao, Guozhi. “Semiparametric Estimators for the Regression Coefficients in the Linear Transformation Competing Risks Models with Missing Cause of Failure.” 2006. Web. 09 Dec 2019.

Vancouver:

Gao G. Semiparametric Estimators for the Regression Coefficients in the Linear Transformation Competing Risks Models with Missing Cause of Failure. [Internet] [Doctoral dissertation]. North Carolina State University; 2006. [cited 2019 Dec 09]. Available from: http://www.lib.ncsu.edu/resolver/1840.16/5228.

Council of Science Editors:

Gao G. Semiparametric Estimators for the Regression Coefficients in the Linear Transformation Competing Risks Models with Missing Cause of Failure. [Doctoral Dissertation]. North Carolina State University; 2006. Available from: http://www.lib.ncsu.edu/resolver/1840.16/5228

22. Zangeneh, Sahar Zohouri. Model-based Methods for Robust Finite Population Inference in the Presence of External Information.

Degree: PhD, Statistics, 2012, University of Michigan

URL: http://hdl.handle.net/2027.42/96005

► This dissertation develops new model-based approaches for analysis of sample survey data. The main focus of the thesis is to incorporate information available from external…
(more)

Subjects/Keywords: Dirichlet Process Priors; Survey Nonresponse; Probability Proportional to Size (PPS); Bayesian Inference; Missing Not at Random (MNAR); Imputation; Statistics and Numeric Data; Science

…to missing-data mechanisms that are missing not at *random* (MNAR), which generally… …it eliminates selection bias (S¨
arndal et al., 2003).
Simple *random* sampling… …be *random* variables. In this approach,
inferences are based on the joint distribution of Y… …missing
data mechanisms into three categories. Data is said to be Missing Completely at
*Random*… …the observed and missing values. On the other hand, data is said to be
Missing Not at *Random*…

Record Details Similar Records

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

APA (6^{th} Edition):

Zangeneh, S. Z. (2012). Model-based Methods for Robust Finite Population Inference in the Presence of External Information. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/96005

Chicago Manual of Style (16^{th} Edition):

Zangeneh, Sahar Zohouri. “Model-based Methods for Robust Finite Population Inference in the Presence of External Information.” 2012. Doctoral Dissertation, University of Michigan. Accessed December 09, 2019. http://hdl.handle.net/2027.42/96005.

MLA Handbook (7^{th} Edition):

Zangeneh, Sahar Zohouri. “Model-based Methods for Robust Finite Population Inference in the Presence of External Information.” 2012. Web. 09 Dec 2019.

Vancouver:

Zangeneh SZ. Model-based Methods for Robust Finite Population Inference in the Presence of External Information. [Internet] [Doctoral dissertation]. University of Michigan; 2012. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/2027.42/96005.

Council of Science Editors:

Zangeneh SZ. Model-based Methods for Robust Finite Population Inference in the Presence of External Information. [Doctoral Dissertation]. University of Michigan; 2012. Available from: http://hdl.handle.net/2027.42/96005

23.
Dipita, Theophile B.
Missing Data in Clinical Trial: A Critical Look at the Proportionality of MNAR and MAR Assumptions for Multiple * Imputation*.

Degree: Doctor of Public Health in Biostatistics (Dr.P.H.), Department of Biostatistics (COPH), 2016, Georgia Southern University

URL: https://digitalcommons.georgiasouthern.edu/etd/1483

► Randomized control trial is a gold standard of research studies. Randomization helps reduce bias and infer causality. One constraint of these studies is that…
(more)

Subjects/Keywords: Missing at random; Missing not at random; Influential exponential tilting; Multiple imputation; Biostatistics; Clinical Trials; Statistical Methodology; Jack N. Averitt College of Graduate Studies, Electronic Theses & Dissertations, ETDs, Student Research

…single *imputation*
methods usually assume missing completely at *random* mechanism which is a more… …of
multiple data sets. The advantages of multiple *imputation* are that a *random* draw of… …33
METHOD OF MULTIPLE *IMPUTATION*… …92
8
LIST OF FIGURES
Figure 1: Schematization of Multiple *Imputation*. The question marks… …results (Tabachnick & Fidel, 2007; Carpenter & Kenward, 2013). Although
no *imputation*…

Record Details Similar Records

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

APA (6^{th} Edition):

Dipita, T. B. (2016). Missing Data in Clinical Trial: A Critical Look at the Proportionality of MNAR and MAR Assumptions for Multiple Imputation. (Doctoral Dissertation). Georgia Southern University. Retrieved from https://digitalcommons.georgiasouthern.edu/etd/1483

Chicago Manual of Style (16^{th} Edition):

Dipita, Theophile B. “Missing Data in Clinical Trial: A Critical Look at the Proportionality of MNAR and MAR Assumptions for Multiple Imputation.” 2016. Doctoral Dissertation, Georgia Southern University. Accessed December 09, 2019. https://digitalcommons.georgiasouthern.edu/etd/1483.

MLA Handbook (7^{th} Edition):

Dipita, Theophile B. “Missing Data in Clinical Trial: A Critical Look at the Proportionality of MNAR and MAR Assumptions for Multiple Imputation.” 2016. Web. 09 Dec 2019.

Vancouver:

Dipita TB. Missing Data in Clinical Trial: A Critical Look at the Proportionality of MNAR and MAR Assumptions for Multiple Imputation. [Internet] [Doctoral dissertation]. Georgia Southern University; 2016. [cited 2019 Dec 09]. Available from: https://digitalcommons.georgiasouthern.edu/etd/1483.

Council of Science Editors:

Dipita TB. Missing Data in Clinical Trial: A Critical Look at the Proportionality of MNAR and MAR Assumptions for Multiple Imputation. [Doctoral Dissertation]. Georgia Southern University; 2016. Available from: https://digitalcommons.georgiasouthern.edu/etd/1483