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You searched for +publisher:"University of Michigan" +contributor:("Little, Roderick J."). Showing records 1 – 30 of 32 total matches.

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University of Michigan

1. Yang, Ye. Robust Methods for Estimating the Mean with Missing Data.

Degree: PhD, Biostatistics, 2015, University of Michigan

 Missing data are common in many empirical studies. In this dissertation, we explore robust methods to estimate the mean of an outcome variable subject to… (more)

Subjects/Keywords: missing data; doubly robust; Statistics and Numeric Data; Science

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APA (6th Edition):

Yang, Y. (2015). Robust Methods for Estimating the Mean with Missing Data. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/113402

Chicago Manual of Style (16th Edition):

Yang, Ye. “Robust Methods for Estimating the Mean with Missing Data.” 2015. Doctoral Dissertation, University of Michigan. Accessed January 26, 2020. http://hdl.handle.net/2027.42/113402.

MLA Handbook (7th Edition):

Yang, Ye. “Robust Methods for Estimating the Mean with Missing Data.” 2015. Web. 26 Jan 2020.

Vancouver:

Yang Y. Robust Methods for Estimating the Mean with Missing Data. [Internet] [Doctoral dissertation]. University of Michigan; 2015. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2027.42/113402.

Council of Science Editors:

Yang Y. Robust Methods for Estimating the Mean with Missing Data. [Doctoral Dissertation]. University of Michigan; 2015. Available from: http://hdl.handle.net/2027.42/113402


University of Michigan

2. Guan, Weihua. Models and Methods for Genome-Wide Association Studies.

Degree: PhD, Biostatistics, 2010, University of Michigan

 Genome-wide association (GWA) studies provide an extensive assessment of common genetic variants across the human genome for disease association. However, due to variation in allele… (more)

Subjects/Keywords: Statistical Genetics; Population Stratification; Genome-wide Association; Rare Variant; Health Sciences; Science

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APA (6th Edition):

Guan, W. (2010). Models and Methods for Genome-Wide Association Studies. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/77921

Chicago Manual of Style (16th Edition):

Guan, Weihua. “Models and Methods for Genome-Wide Association Studies.” 2010. Doctoral Dissertation, University of Michigan. Accessed January 26, 2020. http://hdl.handle.net/2027.42/77921.

MLA Handbook (7th Edition):

Guan, Weihua. “Models and Methods for Genome-Wide Association Studies.” 2010. Web. 26 Jan 2020.

Vancouver:

Guan W. Models and Methods for Genome-Wide Association Studies. [Internet] [Doctoral dissertation]. University of Michigan; 2010. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2027.42/77921.

Council of Science Editors:

Guan W. Models and Methods for Genome-Wide Association Studies. [Doctoral Dissertation]. University of Michigan; 2010. Available from: http://hdl.handle.net/2027.42/77921


University of Michigan

3. Ahn, Jaeil. Bayesian Modeling of Epidemiologic Data under Complex Sampling Schemes.

Degree: PhD, Biostatistics, 2011, University of Michigan

 Case-control studies are dominant analytic tools in epidemiologic research for identifying potential risk factors of a disease. We explore three atypical data situations under a… (more)

Subjects/Keywords: Bayesian Case Control; Spatio-Temporal; Two Phase; Public Health; Health Sciences

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APA (6th Edition):

Ahn, J. (2011). Bayesian Modeling of Epidemiologic Data under Complex Sampling Schemes. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/89687

Chicago Manual of Style (16th Edition):

Ahn, Jaeil. “Bayesian Modeling of Epidemiologic Data under Complex Sampling Schemes.” 2011. Doctoral Dissertation, University of Michigan. Accessed January 26, 2020. http://hdl.handle.net/2027.42/89687.

MLA Handbook (7th Edition):

Ahn, Jaeil. “Bayesian Modeling of Epidemiologic Data under Complex Sampling Schemes.” 2011. Web. 26 Jan 2020.

Vancouver:

Ahn J. Bayesian Modeling of Epidemiologic Data under Complex Sampling Schemes. [Internet] [Doctoral dissertation]. University of Michigan; 2011. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2027.42/89687.

Council of Science Editors:

Ahn J. Bayesian Modeling of Epidemiologic Data under Complex Sampling Schemes. [Doctoral Dissertation]. University of Michigan; 2011. Available from: http://hdl.handle.net/2027.42/89687


University of Michigan

4. Guo, Ying. Multiple Imputation for Measurement Error Correction Based on a Calibration Sample.

Degree: PhD, Biostatistics, 2010, University of Michigan

 In much of applied statistics variables of interest are measured with error. In particular, regression with covariates that are subject to measurement error requires adjustment… (more)

Subjects/Keywords: Missing Data; Measurement Error; Multiple Imputation; Public Health; Statistics and Numeric Data; Health Sciences; Science

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APA (6th Edition):

Guo, Y. (2010). Multiple Imputation for Measurement Error Correction Based on a Calibration Sample. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/77676

Chicago Manual of Style (16th Edition):

Guo, Ying. “Multiple Imputation for Measurement Error Correction Based on a Calibration Sample.” 2010. Doctoral Dissertation, University of Michigan. Accessed January 26, 2020. http://hdl.handle.net/2027.42/77676.

MLA Handbook (7th Edition):

Guo, Ying. “Multiple Imputation for Measurement Error Correction Based on a Calibration Sample.” 2010. Web. 26 Jan 2020.

Vancouver:

Guo Y. Multiple Imputation for Measurement Error Correction Based on a Calibration Sample. [Internet] [Doctoral dissertation]. University of Michigan; 2010. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2027.42/77676.

Council of Science Editors:

Guo Y. Multiple Imputation for Measurement Error Correction Based on a Calibration Sample. [Doctoral Dissertation]. University of Michigan; 2010. Available from: http://hdl.handle.net/2027.42/77676


University of Michigan

5. Huang, Xiaobi. Innovative Statistical Models for Inference from Complex Design Surveys and Longitudinal Studies.

Degree: PhD, Biostatistics, 2011, University of Michigan

 Bayesian inference is a method of statistical inference which combines two sources of information, prior information and data, into the posterior distribution. It is widely… (more)

Subjects/Keywords: Bayesian; Weight Smoothing; Changepoint Model; Missing Data; Women's Reproductive Study; Public Health; Health Sciences

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APA (6th Edition):

Huang, X. (2011). Innovative Statistical Models for Inference from Complex Design Surveys and Longitudinal Studies. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/84514

Chicago Manual of Style (16th Edition):

Huang, Xiaobi. “Innovative Statistical Models for Inference from Complex Design Surveys and Longitudinal Studies.” 2011. Doctoral Dissertation, University of Michigan. Accessed January 26, 2020. http://hdl.handle.net/2027.42/84514.

MLA Handbook (7th Edition):

Huang, Xiaobi. “Innovative Statistical Models for Inference from Complex Design Surveys and Longitudinal Studies.” 2011. Web. 26 Jan 2020.

Vancouver:

Huang X. Innovative Statistical Models for Inference from Complex Design Surveys and Longitudinal Studies. [Internet] [Doctoral dissertation]. University of Michigan; 2011. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2027.42/84514.

Council of Science Editors:

Huang X. Innovative Statistical Models for Inference from Complex Design Surveys and Longitudinal Studies. [Doctoral Dissertation]. University of Michigan; 2011. Available from: http://hdl.handle.net/2027.42/84514


University of Michigan

6. Campbell, Christine Marie Pierce. Assessment of Cervical Cancer Incidence, Histopathology, and Screening Practices Among Hispanic Women in Latin America and Michigan.

Degree: PhD, Epidemiological Science, 2011, University of Michigan

 Cervical cancer is the third most common cancer among women worldwide, with 85% of its global burden occurring in less-developed countries. Although incidence rates of… (more)

Subjects/Keywords: Epidemiology; Cervical Cancer; Latin America and the Caribbean; Hispanics; Cancer Registries; Public Health; Health Sciences

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APA (6th Edition):

Campbell, C. M. P. (2011). Assessment of Cervical Cancer Incidence, Histopathology, and Screening Practices Among Hispanic Women in Latin America and Michigan. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/86559

Chicago Manual of Style (16th Edition):

Campbell, Christine Marie Pierce. “Assessment of Cervical Cancer Incidence, Histopathology, and Screening Practices Among Hispanic Women in Latin America and Michigan.” 2011. Doctoral Dissertation, University of Michigan. Accessed January 26, 2020. http://hdl.handle.net/2027.42/86559.

MLA Handbook (7th Edition):

Campbell, Christine Marie Pierce. “Assessment of Cervical Cancer Incidence, Histopathology, and Screening Practices Among Hispanic Women in Latin America and Michigan.” 2011. Web. 26 Jan 2020.

Vancouver:

Campbell CMP. Assessment of Cervical Cancer Incidence, Histopathology, and Screening Practices Among Hispanic Women in Latin America and Michigan. [Internet] [Doctoral dissertation]. University of Michigan; 2011. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2027.42/86559.

Council of Science Editors:

Campbell CMP. Assessment of Cervical Cancer Incidence, Histopathology, and Screening Practices Among Hispanic Women in Latin America and Michigan. [Doctoral Dissertation]. University of Michigan; 2011. Available from: http://hdl.handle.net/2027.42/86559


University of Michigan

7. Beesley, Lauren. Missing Data and Variable Selection Methods for Cure Models in Cancer Research.

Degree: PhD, Biostatistics, 2018, University of Michigan

 In survival analysis, a common assumption is that all subjects will eventually experience the event of interest given long enough follow-up time. However, there are… (more)

Subjects/Keywords: cure models; multiple imputation; cancer modeling; Public Health; Health Sciences

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APA (6th Edition):

Beesley, L. (2018). Missing Data and Variable Selection Methods for Cure Models in Cancer Research. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/144010

Chicago Manual of Style (16th Edition):

Beesley, Lauren. “Missing Data and Variable Selection Methods for Cure Models in Cancer Research.” 2018. Doctoral Dissertation, University of Michigan. Accessed January 26, 2020. http://hdl.handle.net/2027.42/144010.

MLA Handbook (7th Edition):

Beesley, Lauren. “Missing Data and Variable Selection Methods for Cure Models in Cancer Research.” 2018. Web. 26 Jan 2020.

Vancouver:

Beesley L. Missing Data and Variable Selection Methods for Cure Models in Cancer Research. [Internet] [Doctoral dissertation]. University of Michigan; 2018. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2027.42/144010.

Council of Science Editors:

Beesley L. Missing Data and Variable Selection Methods for Cure Models in Cancer Research. [Doctoral Dissertation]. University of Michigan; 2018. Available from: http://hdl.handle.net/2027.42/144010


University of Michigan

8. Imbriano, Paul. Methods for Improving Efficiency of Planned Missing Data Designs.

Degree: PhD, Biostatistics, 2018, University of Michigan

 Any survey specifically constructed so that at least some variables are unobserved on a subset of participants is a planned missing data design, where missing… (more)

Subjects/Keywords: planned missing data; two-phase sampling; split questionnaire design; Statistics and Numeric Data; Science

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APA (6th Edition):

Imbriano, P. (2018). Methods for Improving Efficiency of Planned Missing Data Designs. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/144155

Chicago Manual of Style (16th Edition):

Imbriano, Paul. “Methods for Improving Efficiency of Planned Missing Data Designs.” 2018. Doctoral Dissertation, University of Michigan. Accessed January 26, 2020. http://hdl.handle.net/2027.42/144155.

MLA Handbook (7th Edition):

Imbriano, Paul. “Methods for Improving Efficiency of Planned Missing Data Designs.” 2018. Web. 26 Jan 2020.

Vancouver:

Imbriano P. Methods for Improving Efficiency of Planned Missing Data Designs. [Internet] [Doctoral dissertation]. University of Michigan; 2018. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2027.42/144155.

Council of Science Editors:

Imbriano P. Methods for Improving Efficiency of Planned Missing Data Designs. [Doctoral Dissertation]. University of Michigan; 2018. Available from: http://hdl.handle.net/2027.42/144155

9. Zhou, Tingting. Robust Methods for Causal Inference Using Penalized Splines.

Degree: PhD, Biostatistics, 2018, University of Michigan

 Observational studies are important for evaluating treatment effects, especially when randomization of treatments is unethical or expensive. Without randomization, valid inferences about treatment effects can… (more)

Subjects/Keywords: causal inference; penalized spline; PENCOMP; Statistics and Numeric Data; Science

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APA (6th Edition):

Zhou, T. (2018). Robust Methods for Causal Inference Using Penalized Splines. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/147507

Chicago Manual of Style (16th Edition):

Zhou, Tingting. “Robust Methods for Causal Inference Using Penalized Splines.” 2018. Doctoral Dissertation, University of Michigan. Accessed January 26, 2020. http://hdl.handle.net/2027.42/147507.

MLA Handbook (7th Edition):

Zhou, Tingting. “Robust Methods for Causal Inference Using Penalized Splines.” 2018. Web. 26 Jan 2020.

Vancouver:

Zhou T. Robust Methods for Causal Inference Using Penalized Splines. [Internet] [Doctoral dissertation]. University of Michigan; 2018. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2027.42/147507.

Council of Science Editors:

Zhou T. Robust Methods for Causal Inference Using Penalized Splines. [Doctoral Dissertation]. University of Michigan; 2018. Available from: http://hdl.handle.net/2027.42/147507

10. Nishimura, Raphael. Substitution of Nonresponding Units in Probability Sampling.

Degree: PhD, Survey Methodology, 2015, University of Michigan

 The substitution of a nonresponding unit with one not originally selected in the sample is a commonly used method for dealing with unit nonresponse. Although… (more)

Subjects/Keywords: Substitution; Nonresponse; Survey Sampling; Missing Data; Responsive Design; Statistics and Numeric Data; Science; Social Sciences

of Michigan. Baldissera, S., Ferrante, G., Quarchioni, E., Minardi, V., Possenti, V… …Seven Years: Design and Procedures. Ann Arbor, MI. Institute for Social Research, University… 

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APA (6th Edition):

Nishimura, R. (2015). Substitution of Nonresponding Units in Probability Sampling. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/113439

Chicago Manual of Style (16th Edition):

Nishimura, Raphael. “Substitution of Nonresponding Units in Probability Sampling.” 2015. Doctoral Dissertation, University of Michigan. Accessed January 26, 2020. http://hdl.handle.net/2027.42/113439.

MLA Handbook (7th Edition):

Nishimura, Raphael. “Substitution of Nonresponding Units in Probability Sampling.” 2015. Web. 26 Jan 2020.

Vancouver:

Nishimura R. Substitution of Nonresponding Units in Probability Sampling. [Internet] [Doctoral dissertation]. University of Michigan; 2015. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2027.42/113439.

Council of Science Editors:

Nishimura R. Substitution of Nonresponding Units in Probability Sampling. [Doctoral Dissertation]. University of Michigan; 2015. Available from: http://hdl.handle.net/2027.42/113439

11. 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

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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

12. Chen, Qixuan. Bayesian Predictive Inference for Three Topics in Survey Samples.

Degree: PhD, Biostatistics, 2009, University of Michigan

 In this thesis, I study three problems in survey samples: inference for finite population quantities in unequal probability sampling, variable selection for multiply imputed data,… (more)

Subjects/Keywords: Bayesian Inference; Limit of Detection; Multiple Imputation; Sample Surveys; Spline Models; Unequal Probability Sampling; Statistics and Numeric Data; Science

…imputation, which are motivated by the University of Michigan Dioxin Exposure Study (UMDES… 

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APA (6th Edition):

Chen, Q. (2009). Bayesian Predictive Inference for Three Topics in Survey Samples. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/64741

Chicago Manual of Style (16th Edition):

Chen, Qixuan. “Bayesian Predictive Inference for Three Topics in Survey Samples.” 2009. Doctoral Dissertation, University of Michigan. Accessed January 26, 2020. http://hdl.handle.net/2027.42/64741.

MLA Handbook (7th Edition):

Chen, Qixuan. “Bayesian Predictive Inference for Three Topics in Survey Samples.” 2009. Web. 26 Jan 2020.

Vancouver:

Chen Q. Bayesian Predictive Inference for Three Topics in Survey Samples. [Internet] [Doctoral dissertation]. University of Michigan; 2009. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2027.42/64741.

Council of Science Editors:

Chen Q. Bayesian Predictive Inference for Three Topics in Survey Samples. [Doctoral Dissertation]. University of Michigan; 2009. Available from: http://hdl.handle.net/2027.42/64741

13. Andridge, Rebecca Roberts. Statistical Methods for Missing Data in Complex Sample Surveys.

Degree: PhD, Biostatistics, 2009, University of Michigan

 Missing data are a pervasive problem in large-scale surveys, arising when a sampled unit does not respond to a particular question or to the entire… (more)

Subjects/Keywords: Missing Data; Sample Surveys; Nonignorable Nonresponse; Hot Deck Imputation; Statistics and Numeric Data; Science

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APA (6th Edition):

Andridge, R. R. (2009). Statistical Methods for Missing Data in Complex Sample Surveys. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/63657

Chicago Manual of Style (16th Edition):

Andridge, Rebecca Roberts. “Statistical Methods for Missing Data in Complex Sample Surveys.” 2009. Doctoral Dissertation, University of Michigan. Accessed January 26, 2020. http://hdl.handle.net/2027.42/63657.

MLA Handbook (7th Edition):

Andridge, Rebecca Roberts. “Statistical Methods for Missing Data in Complex Sample Surveys.” 2009. Web. 26 Jan 2020.

Vancouver:

Andridge RR. Statistical Methods for Missing Data in Complex Sample Surveys. [Internet] [Doctoral dissertation]. University of Michigan; 2009. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2027.42/63657.

Council of Science Editors:

Andridge RR. Statistical Methods for Missing Data in Complex Sample Surveys. [Doctoral Dissertation]. University of Michigan; 2009. Available from: http://hdl.handle.net/2027.42/63657

14. Sakshaug, Joseph Walter. Synthetic Data for Small Area Estimation.

Degree: PhD, Survey Methodology, 2011, University of Michigan

 Small area estimates provide a critical source of information used by a variety of stakeholders to study human conditions and behavior at the local level.… (more)

Subjects/Keywords: Statistical Disclosure Control, Synthetic Data, Small Area Estimation, Hierarchical Model, Sequential Regression Multiple Imputation; Statistics and Numeric Data; Social Sciences

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APA (6th Edition):

Sakshaug, J. W. (2011). Synthetic Data for Small Area Estimation. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/89610

Chicago Manual of Style (16th Edition):

Sakshaug, Joseph Walter. “Synthetic Data for Small Area Estimation.” 2011. Doctoral Dissertation, University of Michigan. Accessed January 26, 2020. http://hdl.handle.net/2027.42/89610.

MLA Handbook (7th Edition):

Sakshaug, Joseph Walter. “Synthetic Data for Small Area Estimation.” 2011. Web. 26 Jan 2020.

Vancouver:

Sakshaug JW. Synthetic Data for Small Area Estimation. [Internet] [Doctoral dissertation]. University of Michigan; 2011. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2027.42/89610.

Council of Science Editors:

Sakshaug JW. Synthetic Data for Small Area Estimation. [Doctoral Dissertation]. University of Michigan; 2011. Available from: http://hdl.handle.net/2027.42/89610

15. West, Brady Thomas. The Error Properties of Interviewer Observations and their Implications for Nonresponse Adjustment of Survey Estimates.

Degree: PhD, Survey Methodology, 2011, University of Michigan

 Interviewer observations are an important source of auxiliary information in survey research. Interviewers can record observations for all units in a sample, and selected observations… (more)

Subjects/Keywords: Interviewer Observations; Nonresponse Adjustment; Survey Paradata; Error in Auxiliary Variables; Survey Methodology; Pattern-Mixture Models; Statistics and Numeric Data; Social Sciences

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APA (6th Edition):

West, B. T. (2011). The Error Properties of Interviewer Observations and their Implications for Nonresponse Adjustment of Survey Estimates. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/89715

Chicago Manual of Style (16th Edition):

West, Brady Thomas. “The Error Properties of Interviewer Observations and their Implications for Nonresponse Adjustment of Survey Estimates.” 2011. Doctoral Dissertation, University of Michigan. Accessed January 26, 2020. http://hdl.handle.net/2027.42/89715.

MLA Handbook (7th Edition):

West, Brady Thomas. “The Error Properties of Interviewer Observations and their Implications for Nonresponse Adjustment of Survey Estimates.” 2011. Web. 26 Jan 2020.

Vancouver:

West BT. The Error Properties of Interviewer Observations and their Implications for Nonresponse Adjustment of Survey Estimates. [Internet] [Doctoral dissertation]. University of Michigan; 2011. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2027.42/89715.

Council of Science Editors:

West BT. The Error Properties of Interviewer Observations and their Implications for Nonresponse Adjustment of Survey Estimates. [Doctoral Dissertation]. University of Michigan; 2011. Available from: http://hdl.handle.net/2027.42/89715

16. Zhang, Nanhua. Ignorable and Nonignorable Modeling in Regression with Incomplete Covariates.

Degree: PhD, Biostatistics, 2011, University of Michigan

 Regression analysis is a statistical tool for studying the relationships between outcome and predictor variables. The analysis is often complicated by missing data. Complete-case analysis… (more)

Subjects/Keywords: Missing Data; Nonignorable Modeling; Missing Covariates; Statistics and Numeric Data; Social Sciences

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APA (6th Edition):

Zhang, N. (2011). Ignorable and Nonignorable Modeling in Regression with Incomplete Covariates. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/89828

Chicago Manual of Style (16th Edition):

Zhang, Nanhua. “Ignorable and Nonignorable Modeling in Regression with Incomplete Covariates.” 2011. Doctoral Dissertation, University of Michigan. Accessed January 26, 2020. http://hdl.handle.net/2027.42/89828.

MLA Handbook (7th Edition):

Zhang, Nanhua. “Ignorable and Nonignorable Modeling in Regression with Incomplete Covariates.” 2011. Web. 26 Jan 2020.

Vancouver:

Zhang N. Ignorable and Nonignorable Modeling in Regression with Incomplete Covariates. [Internet] [Doctoral dissertation]. University of Michigan; 2011. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2027.42/89828.

Council of Science Editors:

Zhang N. Ignorable and Nonignorable Modeling in Regression with Incomplete Covariates. [Doctoral Dissertation]. University of Michigan; 2011. Available from: http://hdl.handle.net/2027.42/89828

17. 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

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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

18. Gao, Xin. Causal Modeling with Principal Stratification to Assess Effects of Treatment with Partial Compliance, Noncompliance, and Principal Surrogacy in Longitudinal and Time-to-Event Settings.

Degree: PhD, Biostatistics, 2012, University of Michigan

 Much research in the social and health sciences aims to understand the causal relationship between an intervention and an outcome, and a variety of statistical… (more)

Subjects/Keywords: Causal Modeling; Noncompliance; Partial Compliance; Potential Outcome; Principal Stratification; Surrogacy; Public Health; Health Sciences

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APA (6th Edition):

Gao, X. (2012). Causal Modeling with Principal Stratification to Assess Effects of Treatment with Partial Compliance, Noncompliance, and Principal Surrogacy in Longitudinal and Time-to-Event Settings. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/96016

Chicago Manual of Style (16th Edition):

Gao, Xin. “Causal Modeling with Principal Stratification to Assess Effects of Treatment with Partial Compliance, Noncompliance, and Principal Surrogacy in Longitudinal and Time-to-Event Settings.” 2012. Doctoral Dissertation, University of Michigan. Accessed January 26, 2020. http://hdl.handle.net/2027.42/96016.

MLA Handbook (7th Edition):

Gao, Xin. “Causal Modeling with Principal Stratification to Assess Effects of Treatment with Partial Compliance, Noncompliance, and Principal Surrogacy in Longitudinal and Time-to-Event Settings.” 2012. Web. 26 Jan 2020.

Vancouver:

Gao X. Causal Modeling with Principal Stratification to Assess Effects of Treatment with Partial Compliance, Noncompliance, and Principal Surrogacy in Longitudinal and Time-to-Event Settings. [Internet] [Doctoral dissertation]. University of Michigan; 2012. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2027.42/96016.

Council of Science Editors:

Gao X. Causal Modeling with Principal Stratification to Assess Effects of Treatment with Partial Compliance, Noncompliance, and Principal Surrogacy in Longitudinal and Time-to-Event Settings. [Doctoral Dissertation]. University of Michigan; 2012. Available from: http://hdl.handle.net/2027.42/96016

19. Lee, Shin-jung. Adaptive Design to Adjust for Unit Nonresponse Using an External Micro-level Benchmark.

Degree: PhD, Survey Methodology, 2017, University of Michigan

 Traditional survey design draws a representative sample and implements post-survey weighting adjustments to compensate for nonresponse. When survey participation decline renders respondents nonrepresentative, the effectiveness… (more)

Subjects/Keywords: Benchmarked Sequential Sampling Design; Survey Design; Benchmarked Multiple Imputation; Survey Cost; Survey Respondent Representativeness; Social Sciences (General); Social Sciences

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APA (6th Edition):

Lee, S. (2017). Adaptive Design to Adjust for Unit Nonresponse Using an External Micro-level Benchmark. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/137173

Chicago Manual of Style (16th Edition):

Lee, Shin-jung. “Adaptive Design to Adjust for Unit Nonresponse Using an External Micro-level Benchmark.” 2017. Doctoral Dissertation, University of Michigan. Accessed January 26, 2020. http://hdl.handle.net/2027.42/137173.

MLA Handbook (7th Edition):

Lee, Shin-jung. “Adaptive Design to Adjust for Unit Nonresponse Using an External Micro-level Benchmark.” 2017. Web. 26 Jan 2020.

Vancouver:

Lee S. Adaptive Design to Adjust for Unit Nonresponse Using an External Micro-level Benchmark. [Internet] [Doctoral dissertation]. University of Michigan; 2017. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2027.42/137173.

Council of Science Editors:

Lee S. Adaptive Design to Adjust for Unit Nonresponse Using an External Micro-level Benchmark. [Doctoral Dissertation]. University of Michigan; 2017. Available from: http://hdl.handle.net/2027.42/137173

20. Zhou, Hanzhi. Accounting for Complex Sample Designs in Multiple Imputation Using the Finite Population Bayesian Bootstrap.

Degree: PhD, Survey Methodology, 2014, University of Michigan

 Multiple imputation (MI) is a well-established method to handle item-nonresponse in sample surveys. Survey data obtained from complex sampling designs often involve features that include… (more)

Subjects/Keywords: Multiple Imputation; Statistics and Numeric Data; Social Sciences

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APA (6th Edition):

Zhou, H. (2014). Accounting for Complex Sample Designs in Multiple Imputation Using the Finite Population Bayesian Bootstrap. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/108807

Chicago Manual of Style (16th Edition):

Zhou, Hanzhi. “Accounting for Complex Sample Designs in Multiple Imputation Using the Finite Population Bayesian Bootstrap.” 2014. Doctoral Dissertation, University of Michigan. Accessed January 26, 2020. http://hdl.handle.net/2027.42/108807.

MLA Handbook (7th Edition):

Zhou, Hanzhi. “Accounting for Complex Sample Designs in Multiple Imputation Using the Finite Population Bayesian Bootstrap.” 2014. Web. 26 Jan 2020.

Vancouver:

Zhou H. Accounting for Complex Sample Designs in Multiple Imputation Using the Finite Population Bayesian Bootstrap. [Internet] [Doctoral dissertation]. University of Michigan; 2014. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2027.42/108807.

Council of Science Editors:

Zhou H. Accounting for Complex Sample Designs in Multiple Imputation Using the Finite Population Bayesian Bootstrap. [Doctoral Dissertation]. University of Michigan; 2014. Available from: http://hdl.handle.net/2027.42/108807

21. Xia, Xi. Robust and Efficient Methods for Bayesian Finite Population Inference.

Degree: PhD, Biostatistics, 2015, University of Michigan

 Bayesian model-based approaches provide data-driven estimates of population quantity of interest from complex survey data to achieve balance between bias correction and efficiency. We focus… (more)

Subjects/Keywords: Finite Population Bayesian Inference; Weight Pooling; Weight Trimming; Laplace Prior; Weighted Dirichlet Process Mixture Model; Statistics and Numeric Data; Science

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APA (6th Edition):

Xia, X. (2015). Robust and Efficient Methods for Bayesian Finite Population Inference. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/111372

Chicago Manual of Style (16th Edition):

Xia, Xi. “Robust and Efficient Methods for Bayesian Finite Population Inference.” 2015. Doctoral Dissertation, University of Michigan. Accessed January 26, 2020. http://hdl.handle.net/2027.42/111372.

MLA Handbook (7th Edition):

Xia, Xi. “Robust and Efficient Methods for Bayesian Finite Population Inference.” 2015. Web. 26 Jan 2020.

Vancouver:

Xia X. Robust and Efficient Methods for Bayesian Finite Population Inference. [Internet] [Doctoral dissertation]. University of Michigan; 2015. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2027.42/111372.

Council of Science Editors:

Xia X. Robust and Efficient Methods for Bayesian Finite Population Inference. [Doctoral Dissertation]. University of Michigan; 2015. Available from: http://hdl.handle.net/2027.42/111372

22. Shlomi, Shahar. Combining Geostatistical Analysis and Flow-and-Transport Models to Improve Groundwater Contaminant Plume Estimation.

Degree: PhD, Environmental Engineering, 2009, University of Michigan

 Groundwater is an important resource, which is often contaminated. In order to ensure a sustainable supply, groundwater has to be monitored, contaminant plumes must be… (more)

Subjects/Keywords: Groundwater Plume Estimation; Geostatistics; Flow-and-Transport Models; Uncertainty; Groundwater Monitoring; Civil and Environmental Engineering; Engineering

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APA (6th Edition):

Shlomi, S. (2009). Combining Geostatistical Analysis and Flow-and-Transport Models to Improve Groundwater Contaminant Plume Estimation. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/62391

Chicago Manual of Style (16th Edition):

Shlomi, Shahar. “Combining Geostatistical Analysis and Flow-and-Transport Models to Improve Groundwater Contaminant Plume Estimation.” 2009. Doctoral Dissertation, University of Michigan. Accessed January 26, 2020. http://hdl.handle.net/2027.42/62391.

MLA Handbook (7th Edition):

Shlomi, Shahar. “Combining Geostatistical Analysis and Flow-and-Transport Models to Improve Groundwater Contaminant Plume Estimation.” 2009. Web. 26 Jan 2020.

Vancouver:

Shlomi S. Combining Geostatistical Analysis and Flow-and-Transport Models to Improve Groundwater Contaminant Plume Estimation. [Internet] [Doctoral dissertation]. University of Michigan; 2009. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2027.42/62391.

Council of Science Editors:

Shlomi S. Combining Geostatistical Analysis and Flow-and-Transport Models to Improve Groundwater Contaminant Plume Estimation. [Doctoral Dissertation]. University of Michigan; 2009. Available from: http://hdl.handle.net/2027.42/62391

23. Liu, Xiaohong. Imputation and Dynamic Models in Semiparametric Survival Analysis.

Degree: PhD, Biostatistics, 2011, University of Michigan

 This dissertation focuses on two topics in semiparametric statistical methods and their applications in medical science: (1) prediction of patients’ lifetimes based on their risk… (more)

Subjects/Keywords: Multiple Imputation; Dynamic Model; Semiparametric Survival Analysis; Statistics and Numeric Data; Science

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APA (6th Edition):

Liu, X. (2011). Imputation and Dynamic Models in Semiparametric Survival Analysis. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/89657

Chicago Manual of Style (16th Edition):

Liu, Xiaohong. “Imputation and Dynamic Models in Semiparametric Survival Analysis.” 2011. Doctoral Dissertation, University of Michigan. Accessed January 26, 2020. http://hdl.handle.net/2027.42/89657.

MLA Handbook (7th Edition):

Liu, Xiaohong. “Imputation and Dynamic Models in Semiparametric Survival Analysis.” 2011. Web. 26 Jan 2020.

Vancouver:

Liu X. Imputation and Dynamic Models in Semiparametric Survival Analysis. [Internet] [Doctoral dissertation]. University of Michigan; 2011. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2027.42/89657.

Council of Science Editors:

Liu X. Imputation and Dynamic Models in Semiparametric Survival Analysis. [Doctoral Dissertation]. University of Michigan; 2011. Available from: http://hdl.handle.net/2027.42/89657


University of Michigan

24. Shen, Ronglai. Statistical Methods in Cancer Genomics.

Degree: PhD, Biostatistics, 2007, University of Michigan

 Genomic and proteomic experiments have become widely applied in cancer profiling studies over the past decade. The genomics era is marked by the success of… (more)

Subjects/Keywords: Cancer Genomics; Health Sciences

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APA (6th Edition):

Shen, R. (2007). Statistical Methods in Cancer Genomics. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/57619

Chicago Manual of Style (16th Edition):

Shen, Ronglai. “Statistical Methods in Cancer Genomics.” 2007. Doctoral Dissertation, University of Michigan. Accessed January 26, 2020. http://hdl.handle.net/2027.42/57619.

MLA Handbook (7th Edition):

Shen, Ronglai. “Statistical Methods in Cancer Genomics.” 2007. Web. 26 Jan 2020.

Vancouver:

Shen R. Statistical Methods in Cancer Genomics. [Internet] [Doctoral dissertation]. University of Michigan; 2007. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2027.42/57619.

Council of Science Editors:

Shen R. Statistical Methods in Cancer Genomics. [Doctoral Dissertation]. University of Michigan; 2007. Available from: http://hdl.handle.net/2027.42/57619


University of Michigan

25. Zhang, Guangyu. Extensions of the Penalized Spline Propensity Prediction Method of Imputation.

Degree: PhD, Biostatistics, 2007, University of Michigan

Little and An (2004) proposed a penalized spline propensity prediction (PSPP) method of imputation of missing values that yields robust model-based inference under the missing… (more)

Subjects/Keywords: Penalized Spline Propensity Prediction (PSPP) Method of Imputation of Missing Values; Health Sciences

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APA (6th Edition):

Zhang, G. (2007). Extensions of the Penalized Spline Propensity Prediction Method of Imputation. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/57686

Chicago Manual of Style (16th Edition):

Zhang, Guangyu. “Extensions of the Penalized Spline Propensity Prediction Method of Imputation.” 2007. Doctoral Dissertation, University of Michigan. Accessed January 26, 2020. http://hdl.handle.net/2027.42/57686.

MLA Handbook (7th Edition):

Zhang, Guangyu. “Extensions of the Penalized Spline Propensity Prediction Method of Imputation.” 2007. Web. 26 Jan 2020.

Vancouver:

Zhang G. Extensions of the Penalized Spline Propensity Prediction Method of Imputation. [Internet] [Doctoral dissertation]. University of Michigan; 2007. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2027.42/57686.

Council of Science Editors:

Zhang G. Extensions of the Penalized Spline Propensity Prediction Method of Imputation. [Doctoral Dissertation]. University of Michigan; 2007. Available from: http://hdl.handle.net/2027.42/57686


University of Michigan

26. An, Di. Multiple Imputation Methods for Statistical Disclosure Control.

Degree: PhD, Biostatistics, 2008, University of Michigan

 Statistical disclosure control (SDC) is an important consideration in the release of public use data sets. Statistical agencies seek SDC methods that limit risk of… (more)

Subjects/Keywords: Confidentiality; Disclosure Protection; Multiple Imputation; Longitudinal Data; Survival Analysis; Science

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APA (6th Edition):

An, D. (2008). Multiple Imputation Methods for Statistical Disclosure Control. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/58398

Chicago Manual of Style (16th Edition):

An, Di. “Multiple Imputation Methods for Statistical Disclosure Control.” 2008. Doctoral Dissertation, University of Michigan. Accessed January 26, 2020. http://hdl.handle.net/2027.42/58398.

MLA Handbook (7th Edition):

An, Di. “Multiple Imputation Methods for Statistical Disclosure Control.” 2008. Web. 26 Jan 2020.

Vancouver:

An D. Multiple Imputation Methods for Statistical Disclosure Control. [Internet] [Doctoral dissertation]. University of Michigan; 2008. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2027.42/58398.

Council of Science Editors:

An D. Multiple Imputation Methods for Statistical Disclosure Control. [Doctoral Dissertation]. University of Michigan; 2008. Available from: http://hdl.handle.net/2027.42/58398


University of Michigan

27. Li, Yun. Statistical Methods in Surrogate Marker Research for Clinical Trials.

Degree: PhD, Biostatistics, 2008, University of Michigan

 A surrogate marker (S) is often an intermediate physical or laboratory indicator in a disease progression process. It can be measured earlier and cost less… (more)

Subjects/Keywords: Surrogate Marker; Randomized Trial; Treatment Prediction; Meta-Analyais; Empirical Bayes; Counterfactual Model; Bayesian Estimation; Ridge Regression;

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APA (6th Edition):

Li, Y. (2008). Statistical Methods in Surrogate Marker Research for Clinical Trials. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/61692

Chicago Manual of Style (16th Edition):

Li, Yun. “Statistical Methods in Surrogate Marker Research for Clinical Trials.” 2008. Doctoral Dissertation, University of Michigan. Accessed January 26, 2020. http://hdl.handle.net/2027.42/61692.

MLA Handbook (7th Edition):

Li, Yun. “Statistical Methods in Surrogate Marker Research for Clinical Trials.” 2008. Web. 26 Jan 2020.

Vancouver:

Li Y. Statistical Methods in Surrogate Marker Research for Clinical Trials. [Internet] [Doctoral dissertation]. University of Michigan; 2008. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2027.42/61692.

Council of Science Editors:

Li Y. Statistical Methods in Surrogate Marker Research for Clinical Trials. [Doctoral Dissertation]. University of Michigan; 2008. Available from: http://hdl.handle.net/2027.42/61692


University of Michigan

28. Zhou, Yan. Some Topics in Missing Data and Adaptive Confidence Intervals. Some Topics in Missing Data and Adaptive Confidence Intervals.

Degree: PhD, Biostatistics, 2009, University of Michigan

 When data are missing at random, the missing-data mechanism can be ignored but this assumption is not always intuitive for general patterns of missing data.… (more)

Subjects/Keywords: Missing Data; EM Algorithm; Clinical Trials; Noncompliance; Bootstrap; Bayesian; Statistics and Numeric Data; Science

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APA (6th Edition):

Zhou, Y. (2009). Some Topics in Missing Data and Adaptive Confidence Intervals. Some Topics in Missing Data and Adaptive Confidence Intervals. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/63837

Chicago Manual of Style (16th Edition):

Zhou, Yan. “Some Topics in Missing Data and Adaptive Confidence Intervals. Some Topics in Missing Data and Adaptive Confidence Intervals.” 2009. Doctoral Dissertation, University of Michigan. Accessed January 26, 2020. http://hdl.handle.net/2027.42/63837.

MLA Handbook (7th Edition):

Zhou, Yan. “Some Topics in Missing Data and Adaptive Confidence Intervals. Some Topics in Missing Data and Adaptive Confidence Intervals.” 2009. Web. 26 Jan 2020.

Vancouver:

Zhou Y. Some Topics in Missing Data and Adaptive Confidence Intervals. Some Topics in Missing Data and Adaptive Confidence Intervals. [Internet] [Doctoral dissertation]. University of Michigan; 2009. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2027.42/63837.

Council of Science Editors:

Zhou Y. Some Topics in Missing Data and Adaptive Confidence Intervals. Some Topics in Missing Data and Adaptive Confidence Intervals. [Doctoral Dissertation]. University of Michigan; 2009. Available from: http://hdl.handle.net/2027.42/63837


University of Michigan

29. Chakraborty, Bibhas. A Study of Non-regularity in Dynamic Treatment Regimes and Some Design Considerations for Multicomponent Interventions.

Degree: PhD, Statistics, 2009, University of Michigan

 This dissertation investigates two methodological problems. The first problem concerns developing and optimizing multicomponent interventions. The traditional approach to this problem is to conduct a… (more)

Subjects/Keywords: Multicomponent Interventions; Dynamic Treatment Regimes; Non-regularity; Fractional Factorial Design; Soft-threshold Estimator; Empirical Bayes; Statistics and Numeric Data; Science

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APA (6th Edition):

Chakraborty, B. (2009). A Study of Non-regularity in Dynamic Treatment Regimes and Some Design Considerations for Multicomponent Interventions. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/64656

Chicago Manual of Style (16th Edition):

Chakraborty, Bibhas. “A Study of Non-regularity in Dynamic Treatment Regimes and Some Design Considerations for Multicomponent Interventions.” 2009. Doctoral Dissertation, University of Michigan. Accessed January 26, 2020. http://hdl.handle.net/2027.42/64656.

MLA Handbook (7th Edition):

Chakraborty, Bibhas. “A Study of Non-regularity in Dynamic Treatment Regimes and Some Design Considerations for Multicomponent Interventions.” 2009. Web. 26 Jan 2020.

Vancouver:

Chakraborty B. A Study of Non-regularity in Dynamic Treatment Regimes and Some Design Considerations for Multicomponent Interventions. [Internet] [Doctoral dissertation]. University of Michigan; 2009. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2027.42/64656.

Council of Science Editors:

Chakraborty B. A Study of Non-regularity in Dynamic Treatment Regimes and Some Design Considerations for Multicomponent Interventions. [Doctoral Dissertation]. University of Michigan; 2009. Available from: http://hdl.handle.net/2027.42/64656


University of Michigan

30. Olson, Kristen M. An Investigation of the Nonresponse - Measurement Error Nexus.

Degree: PhD, Survey Methodology, 2007, University of Michigan

 This dissertation examines the nexus between nonresponse and measurement errors in sample surveys. Recent research has shown no strong relationship between nonresponse rates and nonresponse… (more)

Subjects/Keywords: Survey Nonresponse; Measurement Error; Survey Methodology; Response Propensity; Mathematics; Social Sciences (General); Science; Social Sciences

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APA (6th Edition):

Olson, K. M. (2007). An Investigation of the Nonresponse - Measurement Error Nexus. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/57631

Chicago Manual of Style (16th Edition):

Olson, Kristen M. “An Investigation of the Nonresponse - Measurement Error Nexus.” 2007. Doctoral Dissertation, University of Michigan. Accessed January 26, 2020. http://hdl.handle.net/2027.42/57631.

MLA Handbook (7th Edition):

Olson, Kristen M. “An Investigation of the Nonresponse - Measurement Error Nexus.” 2007. Web. 26 Jan 2020.

Vancouver:

Olson KM. An Investigation of the Nonresponse - Measurement Error Nexus. [Internet] [Doctoral dissertation]. University of Michigan; 2007. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2027.42/57631.

Council of Science Editors:

Olson KM. An Investigation of the Nonresponse - Measurement Error Nexus. [Doctoral Dissertation]. University of Michigan; 2007. Available from: http://hdl.handle.net/2027.42/57631

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