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You searched for subject:(missing at random). Showing records 1 – 26 of 26 total matches.

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University of Adelaide

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

Degree: 2017, University of Adelaide

 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 (6th Edition):

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

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):

Sullivan, Thomas Richard. “Multiple Imputation for Handling Missing Outcome Data.” 2017. Thesis, University of Adelaide. Accessed January 26, 2020. http://hdl.handle.net/2440/119248.

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

MLA Handbook (7th Edition):

Sullivan, Thomas Richard. “Multiple Imputation for Handling Missing Outcome Data.” 2017. Web. 26 Jan 2020.

Vancouver:

Sullivan TR. Multiple Imputation for Handling Missing Outcome Data. [Internet] [Thesis]. University of Adelaide; 2017. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2440/119248.

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

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


East Tennessee State University

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

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

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 (6th 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 (16th Edition):

Oketch, Tobias O. “Performance of Imputation Algorithms on Artificially Produced Missing at Random Data.” 2017. Masters Thesis, East Tennessee State University. Accessed January 26, 2020. https://dc.etsu.edu/etd/3217.

MLA Handbook (7th Edition):

Oketch, Tobias O. “Performance of Imputation Algorithms on Artificially Produced Missing at Random Data.” 2017. Web. 26 Jan 2020.

Vancouver:

Oketch TO. Performance of Imputation Algorithms on Artificially Produced Missing at Random Data. [Internet] [Masters thesis]. East Tennessee State University; 2017. [cited 2020 Jan 26]. 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


University of Arizona

3. Jia, Ziyue. A Nonparametric Multiple Imputation Approach For MNAR Mechanism Using the Sample Selection Model Framework .

Degree: 2019, University of Arizona

Missing data is an unavoidable issue when performing data analysis. If the missing probability is related to unobserved variables, missingness is considered as missing not… (more)

Subjects/Keywords: Missing data; Missing not at random (MNAR); Model misspecification; Sample selection model

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APA (6th Edition):

Jia, Z. (2019). A Nonparametric Multiple Imputation Approach For MNAR Mechanism Using the Sample Selection Model Framework . (Masters Thesis). University of Arizona. Retrieved from http://hdl.handle.net/10150/632554

Chicago Manual of Style (16th Edition):

Jia, Ziyue. “A Nonparametric Multiple Imputation Approach For MNAR Mechanism Using the Sample Selection Model Framework .” 2019. Masters Thesis, University of Arizona. Accessed January 26, 2020. http://hdl.handle.net/10150/632554.

MLA Handbook (7th Edition):

Jia, Ziyue. “A Nonparametric Multiple Imputation Approach For MNAR Mechanism Using the Sample Selection Model Framework .” 2019. Web. 26 Jan 2020.

Vancouver:

Jia Z. A Nonparametric Multiple Imputation Approach For MNAR Mechanism Using the Sample Selection Model Framework . [Internet] [Masters thesis]. University of Arizona; 2019. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/10150/632554.

Council of Science Editors:

Jia Z. A Nonparametric Multiple Imputation Approach For MNAR Mechanism Using the Sample Selection Model Framework . [Masters Thesis]. University of Arizona; 2019. Available from: http://hdl.handle.net/10150/632554


University of KwaZulu-Natal

4. Stephen, Aluko Omololu. Statistical approaches for handling longitudinal and cross sectional discrete data with missing values focusing on multiple imputation and probability weighting.

Degree: 2018, University of KwaZulu-Natal

Abstract available in PDF file. Advisors/Committee Members: Mwambi, Henry Godwell. (advisor).

Subjects/Keywords: Missing data.; Longitudinal studies.; Missing completely at random.; Missing at random.

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APA (6th Edition):

Stephen, A. O. (2018). Statistical approaches for handling longitudinal and cross sectional discrete data with missing values focusing on multiple imputation and probability weighting. (Thesis). University of KwaZulu-Natal. Retrieved from https://researchspace.ukzn.ac.za/handle/10413/16318

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):

Stephen, Aluko Omololu. “Statistical approaches for handling longitudinal and cross sectional discrete data with missing values focusing on multiple imputation and probability weighting.” 2018. Thesis, University of KwaZulu-Natal. Accessed January 26, 2020. https://researchspace.ukzn.ac.za/handle/10413/16318.

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

MLA Handbook (7th Edition):

Stephen, Aluko Omololu. “Statistical approaches for handling longitudinal and cross sectional discrete data with missing values focusing on multiple imputation and probability weighting.” 2018. Web. 26 Jan 2020.

Vancouver:

Stephen AO. Statistical approaches for handling longitudinal and cross sectional discrete data with missing values focusing on multiple imputation and probability weighting. [Internet] [Thesis]. University of KwaZulu-Natal; 2018. [cited 2020 Jan 26]. Available from: https://researchspace.ukzn.ac.za/handle/10413/16318.

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

Council of Science Editors:

Stephen AO. Statistical approaches for handling longitudinal and cross sectional discrete data with missing values focusing on multiple imputation and probability weighting. [Thesis]. University of KwaZulu-Natal; 2018. Available from: https://researchspace.ukzn.ac.za/handle/10413/16318

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


University of KwaZulu-Natal

5. [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

 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 (6th 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

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

Chicago Manual of Style (16th Edition):

author], [No. “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 January 26, 2020. http://dx.doi.org/10.1007/s00213-013-3344-x.

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

MLA Handbook (7th Edition):

author], [No. “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. 26 Jan 2020.

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 2020 Jan 26]. Available from: http://dx.doi.org/10.1007/s00213-013-3344-x.

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

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


University of Newcastle

6. 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

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 (6th 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 (16th 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 January 26, 2020. http://hdl.handle.net/1959.13/808716.

MLA Handbook (7th 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. 26 Jan 2020.

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 2020 Jan 26]. 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

7. Bücker, Michael. Statistische Modelle mit nicht-ignorierbar fehlender Zielgröße und Anwendung in der reject inference.

Degree: 2011, Technische Universität Dortmund

 Statistische Modelle zur Prognose von Kreditausfällen vernachlässigen in der Regel die Beobachtungen derjenigen Kunden, denen erst gar kein Kredit gewährt wurde. Denn für diese abgelehnten… (more)

Subjects/Keywords: empirical likelihood; Generalisierte lineare Modelle; Hausman-Test; Kreditscoring; missing not at random; reject inference; 310

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APA (6th Edition):

Bücker, M. (2011). Statistische Modelle mit nicht-ignorierbar fehlender Zielgröße und Anwendung in der reject inference. (Thesis). Technische Universität Dortmund. Retrieved from http://hdl.handle.net/2003/27716

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):

Bücker, Michael. “Statistische Modelle mit nicht-ignorierbar fehlender Zielgröße und Anwendung in der reject inference.” 2011. Thesis, Technische Universität Dortmund. Accessed January 26, 2020. http://hdl.handle.net/2003/27716.

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

MLA Handbook (7th Edition):

Bücker, Michael. “Statistische Modelle mit nicht-ignorierbar fehlender Zielgröße und Anwendung in der reject inference.” 2011. Web. 26 Jan 2020.

Vancouver:

Bücker M. Statistische Modelle mit nicht-ignorierbar fehlender Zielgröße und Anwendung in der reject inference. [Internet] [Thesis]. Technische Universität Dortmund; 2011. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2003/27716.

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

Council of Science Editors:

Bücker M. Statistische Modelle mit nicht-ignorierbar fehlender Zielgröße und Anwendung in der reject inference. [Thesis]. Technische Universität Dortmund; 2011. Available from: http://hdl.handle.net/2003/27716

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


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

  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 (6th 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

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):

Zheng, Xiyu. “SENSITIVITY ANALYSIS IN HANDLING DISCRETE DATA MISSING AT RANDOM IN HIERARCHICAL LINEAR MODELS VIA MULTIVARIATE NORMALITY.” 2016. Thesis, Virginia Commonwealth University. Accessed January 26, 2020. https://scholarscompass.vcu.edu/etd/4403.

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

MLA Handbook (7th Edition):

Zheng, Xiyu. “SENSITIVITY ANALYSIS IN HANDLING DISCRETE DATA MISSING AT RANDOM IN HIERARCHICAL LINEAR MODELS VIA MULTIVARIATE NORMALITY.” 2016. Web. 26 Jan 2020.

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 2020 Jan 26]. Available from: https://scholarscompass.vcu.edu/etd/4403.

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

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


Bowling Green State University

9. Stoll, Kevin Edward. Methodologies for Missing Data with Range Regressions.

Degree: PhD, Statistics, 2019, Bowling Green State University

 A primary focus of this dissertation is to draw inferences about a response variable that is subject to being missing using large samples. When some… (more)

Subjects/Keywords: Statistics; Missing Data; Missing Response; Nonparametric; Range Regression; Nonparametric Range Regression; Propensity Score; Ascendancy; Average Rank; Propensity; Stratification; Regression; Bootstrap; Missing at Random; Double-Robust; Consistency; Almost Sure

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APA (6th Edition):

Stoll, K. E. (2019). Methodologies for Missing Data with Range Regressions. (Doctoral Dissertation). Bowling Green State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1553084421553404

Chicago Manual of Style (16th Edition):

Stoll, Kevin Edward. “Methodologies for Missing Data with Range Regressions.” 2019. Doctoral Dissertation, Bowling Green State University. Accessed January 26, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1553084421553404.

MLA Handbook (7th Edition):

Stoll, Kevin Edward. “Methodologies for Missing Data with Range Regressions.” 2019. Web. 26 Jan 2020.

Vancouver:

Stoll KE. Methodologies for Missing Data with Range Regressions. [Internet] [Doctoral dissertation]. Bowling Green State University; 2019. [cited 2020 Jan 26]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1553084421553404.

Council of Science Editors:

Stoll KE. Methodologies for Missing Data with Range Regressions. [Doctoral Dissertation]. Bowling Green State University; 2019. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1553084421553404


Temple University

10. XIA, QI. Sufficient Dimension Reduction with Missing Data.

Degree: PhD, 2017, Temple University

Statistics

Existing sufficient dimension reduction (SDR) methods typically consider cases with no missing data. The dissertation aims to propose methods to facilitate the SDR methods… (more)

Subjects/Keywords: Statistics;

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APA (6th Edition):

XIA, Q. (2017). Sufficient Dimension Reduction with Missing Data. (Doctoral Dissertation). Temple University. Retrieved from http://digital.library.temple.edu/u?/p245801coll10,469880

Chicago Manual of Style (16th Edition):

XIA, QI. “Sufficient Dimension Reduction with Missing Data.” 2017. Doctoral Dissertation, Temple University. Accessed January 26, 2020. http://digital.library.temple.edu/u?/p245801coll10,469880.

MLA Handbook (7th Edition):

XIA, QI. “Sufficient Dimension Reduction with Missing Data.” 2017. Web. 26 Jan 2020.

Vancouver:

XIA Q. Sufficient Dimension Reduction with Missing Data. [Internet] [Doctoral dissertation]. Temple University; 2017. [cited 2020 Jan 26]. Available from: http://digital.library.temple.edu/u?/p245801coll10,469880.

Council of Science Editors:

XIA Q. Sufficient Dimension Reduction with Missing Data. [Doctoral Dissertation]. Temple University; 2017. Available from: http://digital.library.temple.edu/u?/p245801coll10,469880


Texas A&M University

11. Jin, Lei. Generalized score tests for missing covariate data.

Degree: 2009, Texas A&M University

 In this dissertation, the generalized score tests based on weighted estimating equations are proposed for missing covariate data. Their properties, including the effects of nuisance… (more)

Subjects/Keywords: Data driven method; Generalized score test; Goodness of fit; Nuisance function; Missing at random; Weighted estimating equation.

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APA (6th Edition):

Jin, L. (2009). Generalized score tests for missing covariate data. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-1625

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):

Jin, Lei. “Generalized score tests for missing covariate data.” 2009. Thesis, Texas A&M University. Accessed January 26, 2020. http://hdl.handle.net/1969.1/ETD-TAMU-1625.

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

MLA Handbook (7th Edition):

Jin, Lei. “Generalized score tests for missing covariate data.” 2009. Web. 26 Jan 2020.

Vancouver:

Jin L. Generalized score tests for missing covariate data. [Internet] [Thesis]. Texas A&M University; 2009. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-1625.

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

Council of Science Editors:

Jin L. Generalized score tests for missing covariate data. [Thesis]. Texas A&M University; 2009. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-1625

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

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

Degree: 2012, Texas A&M University

 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… …the assumption of data missing at random. M¨ uller et al. (2006) propose the… …x29;. This is called the Missing At Random (MAR) assumption. The model is studied… …x5D;where the missing structure is Gaussian and the errors have a t2 distribution 100 XIII… …Simulation results showing the MSE for the estimation of E[Y ]where the missing structure… 

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APA (6th 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

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):

Crawford, Scott. “Efficient Estimation in a Regression Model with Missing Responses.” 2012. Thesis, Texas A&M University. Accessed January 26, 2020. http://hdl.handle.net/1969.1/ETD-TAMU-2012-08-11396.

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

MLA Handbook (7th Edition):

Crawford, Scott. “Efficient Estimation in a Regression Model with Missing Responses.” 2012. Web. 26 Jan 2020.

Vancouver:

Crawford S. Efficient Estimation in a Regression Model with Missing Responses. [Internet] [Thesis]. Texas A&M University; 2012. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2012-08-11396.

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

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

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 (6th 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 (16th Edition):

Liublinska, Viktoriia. “Sensitivity Analyses in Empirical Studies Plagued with Missing Data.” 2013. Doctoral Dissertation, Harvard University. Accessed January 26, 2020. http://nrs.harvard.edu/urn-3:HUL.InstRepos:11124841.

MLA Handbook (7th Edition):

Liublinska, Viktoriia. “Sensitivity Analyses in Empirical Studies Plagued with Missing Data.” 2013. Web. 26 Jan 2020.

Vancouver:

Liublinska V. Sensitivity Analyses in Empirical Studies Plagued with Missing Data. [Internet] [Doctoral dissertation]. Harvard University; 2013. [cited 2020 Jan 26]. 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


Arizona State University

14. 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

 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

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APA (6th 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 (16th 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 January 26, 2020. http://repository.asu.edu/items/40705.

MLA Handbook (7th Edition):

Kunze, Katie Lynn. “Multiple Imputation for Two-Level Hierarchical Models with Categorical Variables and Missing at Random Data.” 2016. Web. 26 Jan 2020.

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 2020 Jan 26]. 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

15. Leon, Selene. Semiparametric Efficient Estimation of Treatment Effect in a Pretest-Posttest Study with Missing Data.

Degree: PhD, Statistics, 2004, North Carolina State University

 Inference on treatment effect in a pretest–posttest study is a routine objective in medicine, public health, and other fields, and a number of approaches have… (more)

Subjects/Keywords: Missing at random.; Inverse probability weighting; Influence function; Analysis of covariance

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APA (6th Edition):

Leon, S. (2004). Semiparametric Efficient Estimation of Treatment Effect in a Pretest-Posttest Study with Missing Data. (Doctoral Dissertation). North Carolina State University. Retrieved from http://www.lib.ncsu.edu/resolver/1840.16/3814

Chicago Manual of Style (16th Edition):

Leon, Selene. “Semiparametric Efficient Estimation of Treatment Effect in a Pretest-Posttest Study with Missing Data.” 2004. Doctoral Dissertation, North Carolina State University. Accessed January 26, 2020. http://www.lib.ncsu.edu/resolver/1840.16/3814.

MLA Handbook (7th Edition):

Leon, Selene. “Semiparametric Efficient Estimation of Treatment Effect in a Pretest-Posttest Study with Missing Data.” 2004. Web. 26 Jan 2020.

Vancouver:

Leon S. Semiparametric Efficient Estimation of Treatment Effect in a Pretest-Posttest Study with Missing Data. [Internet] [Doctoral dissertation]. North Carolina State University; 2004. [cited 2020 Jan 26]. Available from: http://www.lib.ncsu.edu/resolver/1840.16/3814.

Council of Science Editors:

Leon S. Semiparametric Efficient Estimation of Treatment Effect in a Pretest-Posttest Study with Missing Data. [Doctoral Dissertation]. North Carolina State University; 2004. Available from: http://www.lib.ncsu.edu/resolver/1840.16/3814


Virginia Tech

16. Boone, Edward L. Bayesian Methodology for Missing Data, Model Selection and Hierarchical Spatial Models with Application to Ecological Data.

Degree: PhD, Statistics, 2003, Virginia Tech

 Ecological data is often fraught with many problems such as Missing Data and Spatial Correlation. In this dissertation we use a data set collected by… (more)

Subjects/Keywords: Ecological Statistics; Bayesian Model Averaging; Hierarchical Models; Missing at Random

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APA (6th Edition):

Boone, E. L. (2003). Bayesian Methodology for Missing Data, Model Selection and Hierarchical Spatial Models with Application to Ecological Data. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/26141

Chicago Manual of Style (16th Edition):

Boone, Edward L. “Bayesian Methodology for Missing Data, Model Selection and Hierarchical Spatial Models with Application to Ecological Data.” 2003. Doctoral Dissertation, Virginia Tech. Accessed January 26, 2020. http://hdl.handle.net/10919/26141.

MLA Handbook (7th Edition):

Boone, Edward L. “Bayesian Methodology for Missing Data, Model Selection and Hierarchical Spatial Models with Application to Ecological Data.” 2003. Web. 26 Jan 2020.

Vancouver:

Boone EL. Bayesian Methodology for Missing Data, Model Selection and Hierarchical Spatial Models with Application to Ecological Data. [Internet] [Doctoral dissertation]. Virginia Tech; 2003. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/10919/26141.

Council of Science Editors:

Boone EL. Bayesian Methodology for Missing Data, Model Selection and Hierarchical Spatial Models with Application to Ecological Data. [Doctoral Dissertation]. Virginia Tech; 2003. Available from: http://hdl.handle.net/10919/26141

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

Degree: Statistics, 2012, Uppsala University

  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

random (MCAR) P( R Z ,  )  P ( R  ) B. Missing at random… …x28;MAR) P( R Z ,  )  P( R Zobs ,  ) C. Missing not at random… …practice. Missing at random (MAR) stands for that the missingness or the distribution… …Such a method is multiple imputation. Missing not at random (MNAR) indicates that… …prediction result is reliable under missing completely at random(14). Besides ad hoc… 

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APA (6th 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

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):

Pan, Wensi. “Comparison of Imputation Methods on Estimating Regression Equation in MNAR Mechanism.” 2012. Thesis, Uppsala University. Accessed January 26, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-175772.

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

MLA Handbook (7th Edition):

Pan, Wensi. “Comparison of Imputation Methods on Estimating Regression Equation in MNAR Mechanism.” 2012. Web. 26 Jan 2020.

Vancouver:

Pan W. Comparison of Imputation Methods on Estimating Regression Equation in MNAR Mechanism. [Internet] [Thesis]. Uppsala University; 2012. [cited 2020 Jan 26]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-175772.

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

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


Arizona State University

18. Baraldi, Amanda Neeche. Planned Missing Data in Mediation Analysis.

Degree: Psychology, 2015, Arizona State University

Subjects/Keywords: Quantitative psychology and psychometrics; MCAR; missing completely at random; planned missing data; statistical mediation

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APA (6th Edition):

Baraldi, A. N. (2015). Planned Missing Data in Mediation Analysis. (Doctoral Dissertation). Arizona State University. Retrieved from http://repository.asu.edu/items/34903

Chicago Manual of Style (16th Edition):

Baraldi, Amanda Neeche. “Planned Missing Data in Mediation Analysis.” 2015. Doctoral Dissertation, Arizona State University. Accessed January 26, 2020. http://repository.asu.edu/items/34903.

MLA Handbook (7th Edition):

Baraldi, Amanda Neeche. “Planned Missing Data in Mediation Analysis.” 2015. Web. 26 Jan 2020.

Vancouver:

Baraldi AN. Planned Missing Data in Mediation Analysis. [Internet] [Doctoral dissertation]. Arizona State University; 2015. [cited 2020 Jan 26]. Available from: http://repository.asu.edu/items/34903.

Council of Science Editors:

Baraldi AN. Planned Missing Data in Mediation Analysis. [Doctoral Dissertation]. Arizona State University; 2015. Available from: http://repository.asu.edu/items/34903

19. Kang, Shan. Treatment Effect Estimation for Randomized Clinical Trials Subject to Noncompliance and Missing Outcomes.

Degree: PhD, Biostatistics, 2014, University of Michigan

 Noncompliance and missing outcomes are common in randomized clinical trials. In this dissertation, we explore treatment arm switching issues for survival data and nonrandom dropout… (more)

Subjects/Keywords: Missing Data; Clinical Trials; Treatment Switching; Noncompliance; Masked Missing Not at Random; Missing Not at Random; Public Health; Statistics and Numeric Data; Health Sciences; Science

…data make the missing at random (MAR) assumption, but in practice, there are often… …trials, we propose a specific missing not at random (MNAR) assumption, which we call… …missing data are missing at random (MAR), in the sense that missingness does not… …completely at random (MCAR). Although CC analysis is the default option in many… …missing values and the MAR assumption is violated. Such mechanisms are called missing not at… 

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

APA (6th Edition):

Kang, S. (2014). Treatment Effect Estimation for Randomized Clinical Trials Subject to Noncompliance and Missing Outcomes. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/110464

Chicago Manual of Style (16th Edition):

Kang, Shan. “Treatment Effect Estimation for Randomized Clinical Trials Subject to Noncompliance and Missing Outcomes.” 2014. Doctoral Dissertation, University of Michigan. Accessed January 26, 2020. http://hdl.handle.net/2027.42/110464.

MLA Handbook (7th Edition):

Kang, Shan. “Treatment Effect Estimation for Randomized Clinical Trials Subject to Noncompliance and Missing Outcomes.” 2014. Web. 26 Jan 2020.

Vancouver:

Kang S. Treatment Effect Estimation for Randomized Clinical Trials Subject to Noncompliance and Missing Outcomes. [Internet] [Doctoral dissertation]. University of Michigan; 2014. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2027.42/110464.

Council of Science Editors:

Kang S. Treatment Effect Estimation for Randomized Clinical Trials Subject to Noncompliance and Missing Outcomes. [Doctoral Dissertation]. University of Michigan; 2014. Available from: http://hdl.handle.net/2027.42/110464


North Carolina State University

20. Yi, Bingming. Nonparametric, Parametric and Semiparametric Models for Screening and Decoding Pools of Chemical Compounds.

Degree: PhD, Statistics, 2002, North Carolina State University

 During High Throughput Screening (HTS), large collections of chemical compounds are tested for potency with respect to one or more assays. In reality, only a… (more)

Subjects/Keywords: drug discovery; HTS; hit rate; cell-based; blocking; pooling; group testing; missing at random; semiparametric models

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APA (6th Edition):

Yi, B. (2002). Nonparametric, Parametric and Semiparametric Models for Screening and Decoding Pools of Chemical Compounds. (Doctoral Dissertation). North Carolina State University. Retrieved from http://www.lib.ncsu.edu/resolver/1840.16/4249

Chicago Manual of Style (16th Edition):

Yi, Bingming. “Nonparametric, Parametric and Semiparametric Models for Screening and Decoding Pools of Chemical Compounds.” 2002. Doctoral Dissertation, North Carolina State University. Accessed January 26, 2020. http://www.lib.ncsu.edu/resolver/1840.16/4249.

MLA Handbook (7th Edition):

Yi, Bingming. “Nonparametric, Parametric and Semiparametric Models for Screening and Decoding Pools of Chemical Compounds.” 2002. Web. 26 Jan 2020.

Vancouver:

Yi B. Nonparametric, Parametric and Semiparametric Models for Screening and Decoding Pools of Chemical Compounds. [Internet] [Doctoral dissertation]. North Carolina State University; 2002. [cited 2020 Jan 26]. Available from: http://www.lib.ncsu.edu/resolver/1840.16/4249.

Council of Science Editors:

Yi B. Nonparametric, Parametric and Semiparametric Models for Screening and Decoding Pools of Chemical Compounds. [Doctoral Dissertation]. North Carolina State University; 2002. Available from: http://www.lib.ncsu.edu/resolver/1840.16/4249


University of Florida

21. Cetin-Berber, Duygu. A Comparison of One-Step and Three-Step Approaches for Including Covariates in the Shared Parameter Growth Mixture Model.

Degree: MA, Research and Evaluation Methodology - Human Development and Organizational Studies in Education, 2016, University of Florida

Subjects/Keywords: analysis; approach; at; data; growth; missing; mixture; model; not; one; parameter; random; shared; step; three

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APA (6th Edition):

Cetin-Berber, D. (2016). A Comparison of One-Step and Three-Step Approaches for Including Covariates in the Shared Parameter Growth Mixture Model. (Masters Thesis). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0050457

Chicago Manual of Style (16th Edition):

Cetin-Berber, Duygu. “A Comparison of One-Step and Three-Step Approaches for Including Covariates in the Shared Parameter Growth Mixture Model.” 2016. Masters Thesis, University of Florida. Accessed January 26, 2020. http://ufdc.ufl.edu/UFE0050457.

MLA Handbook (7th Edition):

Cetin-Berber, Duygu. “A Comparison of One-Step and Three-Step Approaches for Including Covariates in the Shared Parameter Growth Mixture Model.” 2016. Web. 26 Jan 2020.

Vancouver:

Cetin-Berber D. A Comparison of One-Step and Three-Step Approaches for Including Covariates in the Shared Parameter Growth Mixture Model. [Internet] [Masters thesis]. University of Florida; 2016. [cited 2020 Jan 26]. Available from: http://ufdc.ufl.edu/UFE0050457.

Council of Science Editors:

Cetin-Berber D. A Comparison of One-Step and Three-Step Approaches for Including Covariates in the Shared Parameter Growth Mixture Model. [Masters Thesis]. University of Florida; 2016. Available from: http://ufdc.ufl.edu/UFE0050457

22. Jankovic, Dina. Analysis of Longitudinal Data with Missing Responses Adjusted by Inverse Probability Weights .

Degree: 2018, University of Ottawa

 We propose a new method for analyzing longitudinal data which contain responses that are missing at random. This method consists in solving the generalized estimating… (more)

Subjects/Keywords: Longitudinal data; Generalized estimating equations; Asymptotic properties; Missing at random; Inverse probability weights

…responses are missing. We assume that the following Missing at Random Assumption (MAR)… …random and the responses are missing at random. 2. THEORETICAL RESULTS 25 P Lemma 2.4.1… …7] for longitudinal data, in the presence of missing responses. Using this method, we… …we denote by Yij the response of individual i at time j. Some of these responses may be… …missing. We let ( 1, if Yij is observed Iij = 0, if Yij is missing (1) (p… 

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APA (6th Edition):

Jankovic, D. (2018). Analysis of Longitudinal Data with Missing Responses Adjusted by Inverse Probability Weights . (Thesis). University of Ottawa. Retrieved from http://hdl.handle.net/10393/37838

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):

Jankovic, Dina. “Analysis of Longitudinal Data with Missing Responses Adjusted by Inverse Probability Weights .” 2018. Thesis, University of Ottawa. Accessed January 26, 2020. http://hdl.handle.net/10393/37838.

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

MLA Handbook (7th Edition):

Jankovic, Dina. “Analysis of Longitudinal Data with Missing Responses Adjusted by Inverse Probability Weights .” 2018. Web. 26 Jan 2020.

Vancouver:

Jankovic D. Analysis of Longitudinal Data with Missing Responses Adjusted by Inverse Probability Weights . [Internet] [Thesis]. University of Ottawa; 2018. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/10393/37838.

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

Council of Science Editors:

Jankovic D. Analysis of Longitudinal Data with Missing Responses Adjusted by Inverse Probability Weights . [Thesis]. University of Ottawa; 2018. Available from: http://hdl.handle.net/10393/37838

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

23. Riddles, Minsun Kim. Propensity score adjusted method for missing data.

Degree: 2013, Iowa State University

 Propensity score adjustment is a popular technique for handling unit nonresponse in sample surveys. When the response probability does not depend on the study variable… (more)

Subjects/Keywords: Calibration; Exponential tilting; Nonignorable nonresponse; Nonresponse; Not missing at random; Weighting; Statistics and Probability

missing completely at random (MCAR) if the response indicator δ is independent of the… …response mechanism is missing at random, the response mechanism is also called ignorable [… …fails to hold. We have restricted our attention to missing-at-random mechanisms in which the… …weighting adjustment, Nonresponse error, Not missing at random 27 3.1 Introduction Analysis… …observed throughout the sample. A weaker condition for the response mechanism is missing at… 

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APA (6th Edition):

Riddles, M. K. (2013). Propensity score adjusted method for missing data. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/13287

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):

Riddles, Minsun Kim. “Propensity score adjusted method for missing data.” 2013. Thesis, Iowa State University. Accessed January 26, 2020. https://lib.dr.iastate.edu/etd/13287.

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

MLA Handbook (7th Edition):

Riddles, Minsun Kim. “Propensity score adjusted method for missing data.” 2013. Web. 26 Jan 2020.

Vancouver:

Riddles MK. Propensity score adjusted method for missing data. [Internet] [Thesis]. Iowa State University; 2013. [cited 2020 Jan 26]. Available from: https://lib.dr.iastate.edu/etd/13287.

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

Council of Science Editors:

Riddles MK. Propensity score adjusted method for missing data. [Thesis]. Iowa State University; 2013. Available from: https://lib.dr.iastate.edu/etd/13287

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

24. 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

  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… …mechanism that consist of missing completely at random (MCAR), where missingness is… …independent of study variables; missing at random (MAR), where missingness can depend on… …observed variables but not on missing outcomes; and missing not at random (MNAR), where… …under the assumption of missing completely at random. These methods were particularly accepted… 

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APA (6th 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 (16th 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 January 26, 2020. https://digitalcommons.georgiasouthern.edu/etd/1483.

MLA Handbook (7th 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. 26 Jan 2020.

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 2020 Jan 26]. 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


North Carolina State University

25. 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

 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

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APA (6th 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 (16th 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 January 26, 2020. http://www.lib.ncsu.edu/resolver/1840.16/5228.

MLA Handbook (7th Edition):

Gao, Guozhi. “Semiparametric Estimators for the Regression Coefficients in the Linear Transformation Competing Risks Models with Missing Cause of Failure.” 2006. Web. 26 Jan 2020.

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 2020 Jan 26]. 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

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

Degree: PhD, Statistics, 2012, University of Michigan

 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… …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… …these groups, even after conditioning on the observed variables. Missing at Random (MAR… …1, . . . , n, with the εi being independent random variables 15 centered at zero with… 

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APA (6th 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 (16th 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 January 26, 2020. http://hdl.handle.net/2027.42/96005.

MLA Handbook (7th Edition):

Zangeneh, Sahar Zohouri. “Model-based Methods for Robust Finite Population Inference in the Presence of External Information.” 2012. Web. 26 Jan 2020.

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 2020 Jan 26]. 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

.