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You searched for +publisher:"University of Michigan" +contributor:("Murphy, Susan A."). Showing records 1 – 25 of 25 total matches.

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University of Michigan

1. Yan, Huiying. Memory Bookmarking: Using In Situ Information to Promote Recall in Online Data Collection.

Degree: PhD, Survey Methodology, 2020, University of Michigan

 Understanding human behavior is the goal of many social sciences, but accurately measuring the everyday behaviors of individuals is difficult. To collect information about and… (more)

Subjects/Keywords: mobile data collection; memory recall; measurement error; GPS; photo; texting; Social Sciences

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APA (6th Edition):

Yan, H. (2020). Memory Bookmarking: Using In Situ Information to Promote Recall in Online Data Collection. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/155208

Chicago Manual of Style (16th Edition):

Yan, Huiying. “Memory Bookmarking: Using In Situ Information to Promote Recall in Online Data Collection.” 2020. Doctoral Dissertation, University of Michigan. Accessed November 25, 2020. http://hdl.handle.net/2027.42/155208.

MLA Handbook (7th Edition):

Yan, Huiying. “Memory Bookmarking: Using In Situ Information to Promote Recall in Online Data Collection.” 2020. Web. 25 Nov 2020.

Vancouver:

Yan H. Memory Bookmarking: Using In Situ Information to Promote Recall in Online Data Collection. [Internet] [Doctoral dissertation]. University of Michigan; 2020. [cited 2020 Nov 25]. Available from: http://hdl.handle.net/2027.42/155208.

Council of Science Editors:

Yan H. Memory Bookmarking: Using In Situ Information to Promote Recall in Online Data Collection. [Doctoral Dissertation]. University of Michigan; 2020. Available from: http://hdl.handle.net/2027.42/155208


University of Michigan

2. Westgate, Philip Michael. Improving Small-Sample Inference in Group Randomized Trials and Other Sources of Correlated Binary Outcomes.

Degree: PhD, Biostatistics, 2011, University of Michigan

 Group Randomized Trials (GRTs), along with many other types of studies, commonly can be composed of a small to moderate number of independent clusters of… (more)

Subjects/Keywords: Estimating Equations; Estimation Performance; Generalized Estimating Equations; Test Size; Science

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APA (6th Edition):

Westgate, P. M. (2011). Improving Small-Sample Inference in Group Randomized Trials and Other Sources of Correlated Binary Outcomes. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/89710

Chicago Manual of Style (16th Edition):

Westgate, Philip Michael. “Improving Small-Sample Inference in Group Randomized Trials and Other Sources of Correlated Binary Outcomes.” 2011. Doctoral Dissertation, University of Michigan. Accessed November 25, 2020. http://hdl.handle.net/2027.42/89710.

MLA Handbook (7th Edition):

Westgate, Philip Michael. “Improving Small-Sample Inference in Group Randomized Trials and Other Sources of Correlated Binary Outcomes.” 2011. Web. 25 Nov 2020.

Vancouver:

Westgate PM. Improving Small-Sample Inference in Group Randomized Trials and Other Sources of Correlated Binary Outcomes. [Internet] [Doctoral dissertation]. University of Michigan; 2011. [cited 2020 Nov 25]. Available from: http://hdl.handle.net/2027.42/89710.

Council of Science Editors:

Westgate PM. Improving Small-Sample Inference in Group Randomized Trials and Other Sources of Correlated Binary Outcomes. [Doctoral Dissertation]. University of Michigan; 2011. Available from: http://hdl.handle.net/2027.42/89710


University of Michigan

3. Steimle, Lauren. Stochastic Dynamic Optimization Under Ambiguity.

Degree: PhD, Industrial & Operations Engineering, 2019, University of Michigan

 Stochastic dynamic optimization methods are powerful mathematical tools for informing sequential decision-making in environments where the outcomes of decisions are uncertain. For instance, the Markov… (more)

Subjects/Keywords: dynamic programming; Markov decision process; stochastic optimization; decomposition; parameter uncertainty; ambiguity; Industrial and Operations Engineering; Engineering

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APA (6th Edition):

Steimle, L. (2019). Stochastic Dynamic Optimization Under Ambiguity. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/149947

Chicago Manual of Style (16th Edition):

Steimle, Lauren. “Stochastic Dynamic Optimization Under Ambiguity.” 2019. Doctoral Dissertation, University of Michigan. Accessed November 25, 2020. http://hdl.handle.net/2027.42/149947.

MLA Handbook (7th Edition):

Steimle, Lauren. “Stochastic Dynamic Optimization Under Ambiguity.” 2019. Web. 25 Nov 2020.

Vancouver:

Steimle L. Stochastic Dynamic Optimization Under Ambiguity. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2020 Nov 25]. Available from: http://hdl.handle.net/2027.42/149947.

Council of Science Editors:

Steimle L. Stochastic Dynamic Optimization Under Ambiguity. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/149947


University of Michigan

4. Liao, Peng. Just-In-Time Adaptive Interventions: Experiment, Inference and Online Learning.

Degree: PhD, Statistics, 2019, University of Michigan

 The use and development of mobile interventions are experiencing rapid growth. Ideally, mobile devices can be used to provide treatment/support whenever needed and to adapt… (more)

Subjects/Keywords: adaptive intervention; reinforcement learning; experiment design; semi-parametric; mobile health; off-policy evaluation; Statistics and Numeric Data; Science

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APA (6th Edition):

Liao, P. (2019). Just-In-Time Adaptive Interventions: Experiment, Inference and Online Learning. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/151474

Chicago Manual of Style (16th Edition):

Liao, Peng. “Just-In-Time Adaptive Interventions: Experiment, Inference and Online Learning.” 2019. Doctoral Dissertation, University of Michigan. Accessed November 25, 2020. http://hdl.handle.net/2027.42/151474.

MLA Handbook (7th Edition):

Liao, Peng. “Just-In-Time Adaptive Interventions: Experiment, Inference and Online Learning.” 2019. Web. 25 Nov 2020.

Vancouver:

Liao P. Just-In-Time Adaptive Interventions: Experiment, Inference and Online Learning. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2020 Nov 25]. Available from: http://hdl.handle.net/2027.42/151474.

Council of Science Editors:

Liao P. Just-In-Time Adaptive Interventions: Experiment, Inference and Online Learning. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/151474


University of Michigan

5. Laber, Eric B. Adaptive Confidence Intervals for Non-Smooth Functionals.

Degree: PhD, Statistics, 2011, University of Michigan

 Many quantities of interest in modern statistical analysis are non-smooth functionals of the underlying generative distribution, the observed data, or both. Examples include the test… (more)

Subjects/Keywords: Nonregular Functionals; Adaptive Confidence Intervals; Statistics and Numeric Data; Science

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APA (6th Edition):

Laber, E. B. (2011). Adaptive Confidence Intervals for Non-Smooth Functionals. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/84551

Chicago Manual of Style (16th Edition):

Laber, Eric B. “Adaptive Confidence Intervals for Non-Smooth Functionals.” 2011. Doctoral Dissertation, University of Michigan. Accessed November 25, 2020. http://hdl.handle.net/2027.42/84551.

MLA Handbook (7th Edition):

Laber, Eric B. “Adaptive Confidence Intervals for Non-Smooth Functionals.” 2011. Web. 25 Nov 2020.

Vancouver:

Laber EB. Adaptive Confidence Intervals for Non-Smooth Functionals. [Internet] [Doctoral dissertation]. University of Michigan; 2011. [cited 2020 Nov 25]. Available from: http://hdl.handle.net/2027.42/84551.

Council of Science Editors:

Laber EB. Adaptive Confidence Intervals for Non-Smooth Functionals. [Doctoral Dissertation]. University of Michigan; 2011. Available from: http://hdl.handle.net/2027.42/84551


University of Michigan

6. Wu, Tianshuang. Set Valued Dynamic Treatment Regimes.

Degree: PhD, Statistics, 2016, University of Michigan

 Dynamic Treatment Regimes (DTR)s are composed of sequences of decision rules, one per stage of treatment. Each decision rule inputs patient information and outputs a… (more)

Subjects/Keywords: Dynamic treatment Regime; Statistics and Numeric Data; Science

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APA (6th Edition):

Wu, T. (2016). Set Valued Dynamic Treatment Regimes. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/133462

Chicago Manual of Style (16th Edition):

Wu, Tianshuang. “Set Valued Dynamic Treatment Regimes.” 2016. Doctoral Dissertation, University of Michigan. Accessed November 25, 2020. http://hdl.handle.net/2027.42/133462.

MLA Handbook (7th Edition):

Wu, Tianshuang. “Set Valued Dynamic Treatment Regimes.” 2016. Web. 25 Nov 2020.

Vancouver:

Wu T. Set Valued Dynamic Treatment Regimes. [Internet] [Doctoral dissertation]. University of Michigan; 2016. [cited 2020 Nov 25]. Available from: http://hdl.handle.net/2027.42/133462.

Council of Science Editors:

Wu T. Set Valued Dynamic Treatment Regimes. [Doctoral Dissertation]. University of Michigan; 2016. Available from: http://hdl.handle.net/2027.42/133462


University of Michigan

7. Zeng, Donglin. Adjusting for dependent censoring using high-dimensional auxiliary information.

Degree: PhD, Statistics, 2001, University of Michigan

 This dissertation deals with right-censored data in survival analysis, where the dependent censoring is explained by high-dimensional auxiliary information and treatment variables. We focus on… (more)

Subjects/Keywords: Adjusting; Auxiliary Information; Censoring; Cox's Regression; Dependent; Dimensional; High; Semiparametric; Using

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APA (6th Edition):

Zeng, D. (2001). Adjusting for dependent censoring using high-dimensional auxiliary information. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/126889

Chicago Manual of Style (16th Edition):

Zeng, Donglin. “Adjusting for dependent censoring using high-dimensional auxiliary information.” 2001. Doctoral Dissertation, University of Michigan. Accessed November 25, 2020. http://hdl.handle.net/2027.42/126889.

MLA Handbook (7th Edition):

Zeng, Donglin. “Adjusting for dependent censoring using high-dimensional auxiliary information.” 2001. Web. 25 Nov 2020.

Vancouver:

Zeng D. Adjusting for dependent censoring using high-dimensional auxiliary information. [Internet] [Doctoral dissertation]. University of Michigan; 2001. [cited 2020 Nov 25]. Available from: http://hdl.handle.net/2027.42/126889.

Council of Science Editors:

Zeng D. Adjusting for dependent censoring using high-dimensional auxiliary information. [Doctoral Dissertation]. University of Michigan; 2001. Available from: http://hdl.handle.net/2027.42/126889


University of Michigan

8. Almirall, Daniel. Towards assessing time-varying causal effect moderation in experimental and observational studies.

Degree: PhD, Statistics, 2007, University of Michigan

 This dissertation considers statistical methodology for causal effect moderation in both experimental and observational studies, in both the point-treatment and time-varying treatments setting. We define… (more)

Subjects/Keywords: Assessing; Causal Effect Moderation; Estimators; Experimental; Nuisance Functions; Observational; Robin's Structural Nested Mean; Studies; Time-varying Causal Effect; Towards

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APA (6th Edition):

Almirall, D. (2007). Towards assessing time-varying causal effect moderation in experimental and observational studies. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/126620

Chicago Manual of Style (16th Edition):

Almirall, Daniel. “Towards assessing time-varying causal effect moderation in experimental and observational studies.” 2007. Doctoral Dissertation, University of Michigan. Accessed November 25, 2020. http://hdl.handle.net/2027.42/126620.

MLA Handbook (7th Edition):

Almirall, Daniel. “Towards assessing time-varying causal effect moderation in experimental and observational studies.” 2007. Web. 25 Nov 2020.

Vancouver:

Almirall D. Towards assessing time-varying causal effect moderation in experimental and observational studies. [Internet] [Doctoral dissertation]. University of Michigan; 2007. [cited 2020 Nov 25]. Available from: http://hdl.handle.net/2027.42/126620.

Council of Science Editors:

Almirall D. Towards assessing time-varying causal effect moderation in experimental and observational studies. [Doctoral Dissertation]. University of Michigan; 2007. Available from: http://hdl.handle.net/2027.42/126620

9. 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 November 25, 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. 25 Nov 2020.

Vancouver:

Andridge RR. Statistical Methods for Missing Data in Complex Sample Surveys. [Internet] [Doctoral dissertation]. University of Michigan; 2009. [cited 2020 Nov 25]. 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

10. Gunter, Lacey L. Variable Selection for Decision Making.

Degree: PhD, Statistics, 2009, University of Michigan

 In decision making research, scientists collect a large number of variables that may be useful in deciding which action is best. Researchers might use a… (more)

Subjects/Keywords: Decision Making; Variable Selection; Value of Information; Lasso; Statistics and Numeric Data; Science

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APA (6th Edition):

Gunter, L. L. (2009). Variable Selection for Decision Making. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/63808

Chicago Manual of Style (16th Edition):

Gunter, Lacey L. “Variable Selection for Decision Making.” 2009. Doctoral Dissertation, University of Michigan. Accessed November 25, 2020. http://hdl.handle.net/2027.42/63808.

MLA Handbook (7th Edition):

Gunter, Lacey L. “Variable Selection for Decision Making.” 2009. Web. 25 Nov 2020.

Vancouver:

Gunter LL. Variable Selection for Decision Making. [Internet] [Doctoral dissertation]. University of Michigan; 2009. [cited 2020 Nov 25]. Available from: http://hdl.handle.net/2027.42/63808.

Council of Science Editors:

Gunter LL. Variable Selection for Decision Making. [Doctoral Dissertation]. University of Michigan; 2009. Available from: http://hdl.handle.net/2027.42/63808

11. Lu, Xi. Evaluation and Comparison of Dynamic Treatment Regimes: Methods and Challenges.

Degree: PhD, Statistics, 2015, University of Michigan

 Dynamic treatment regimes (DTRs) are sequences of decision rules that link the patient history with treatment recommendations. Clinical scientists have become increasingly interested in the… (more)

Subjects/Keywords: Dynamic treatment regime; Sequential Multiple Assignment Randomized Trial; Statistics and Numeric Data; Science

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APA (6th Edition):

Lu, X. (2015). Evaluation and Comparison of Dynamic Treatment Regimes: Methods and Challenges. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/113451

Chicago Manual of Style (16th Edition):

Lu, Xi. “Evaluation and Comparison of Dynamic Treatment Regimes: Methods and Challenges.” 2015. Doctoral Dissertation, University of Michigan. Accessed November 25, 2020. http://hdl.handle.net/2027.42/113451.

MLA Handbook (7th Edition):

Lu, Xi. “Evaluation and Comparison of Dynamic Treatment Regimes: Methods and Challenges.” 2015. Web. 25 Nov 2020.

Vancouver:

Lu X. Evaluation and Comparison of Dynamic Treatment Regimes: Methods and Challenges. [Internet] [Doctoral dissertation]. University of Michigan; 2015. [cited 2020 Nov 25]. Available from: http://hdl.handle.net/2027.42/113451.

Council of Science Editors:

Lu X. Evaluation and Comparison of Dynamic Treatment Regimes: Methods and Challenges. [Doctoral Dissertation]. University of Michigan; 2015. Available from: http://hdl.handle.net/2027.42/113451

12. Lei, Huitian. An Online Actor Critic Algorithm and a Statistical Decision Procedure for Personalizing Intervention.

Degree: PhD, Statistics, 2016, University of Michigan

 Increasing technological sophistication and widespread use of smartphones and wearable devices provide opportunities for innovative health interventions. An Adaptive Intervention (AI) personalizes the type, mode… (more)

Subjects/Keywords: Just-in-TIme-Adaptive-Intervention; Online Learning; Statistics and Numeric Data; Health Sciences; Science

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APA (6th Edition):

Lei, H. (2016). An Online Actor Critic Algorithm and a Statistical Decision Procedure for Personalizing Intervention. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/133223

Chicago Manual of Style (16th Edition):

Lei, Huitian. “An Online Actor Critic Algorithm and a Statistical Decision Procedure for Personalizing Intervention.” 2016. Doctoral Dissertation, University of Michigan. Accessed November 25, 2020. http://hdl.handle.net/2027.42/133223.

MLA Handbook (7th Edition):

Lei, Huitian. “An Online Actor Critic Algorithm and a Statistical Decision Procedure for Personalizing Intervention.” 2016. Web. 25 Nov 2020.

Vancouver:

Lei H. An Online Actor Critic Algorithm and a Statistical Decision Procedure for Personalizing Intervention. [Internet] [Doctoral dissertation]. University of Michigan; 2016. [cited 2020 Nov 25]. Available from: http://hdl.handle.net/2027.42/133223.

Council of Science Editors:

Lei H. An Online Actor Critic Algorithm and a Statistical Decision Procedure for Personalizing Intervention. [Doctoral Dissertation]. University of Michigan; 2016. Available from: http://hdl.handle.net/2027.42/133223

13. 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 November 25, 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. 25 Nov 2020.

Vancouver:

Zhang N. Ignorable and Nonignorable Modeling in Regression with Incomplete Covariates. [Internet] [Doctoral dissertation]. University of Michigan; 2011. [cited 2020 Nov 25]. 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

14. Barbarin, Andrea. Toward Health Information Technology that Supports Overweight and Obese Women in Addressing Emotion- and Stress-Related Eating (a Mixed Methods Approach).

Degree: PhD, Information, 2017, University of Michigan

 Extensive research shows that negative emotions and stress can prompt eating behavior that is in excess of physiological nutritional needs. Additionally, research indicates that women… (more)

Subjects/Keywords: Health Informatics; Information; Obesity; Women; Stress Eating; Emotional Eating; Information and Library Science; Psychology; Public Health; Women's and Gender Studies; Social Sciences

…x28;e.g., University of Michigan Investigational Weight Management Clinic, MHealthy Ready to… 

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APA (6th Edition):

Barbarin, A. (2017). Toward Health Information Technology that Supports Overweight and Obese Women in Addressing Emotion- and Stress-Related Eating (a Mixed Methods Approach). (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/138457

Chicago Manual of Style (16th Edition):

Barbarin, Andrea. “Toward Health Information Technology that Supports Overweight and Obese Women in Addressing Emotion- and Stress-Related Eating (a Mixed Methods Approach).” 2017. Doctoral Dissertation, University of Michigan. Accessed November 25, 2020. http://hdl.handle.net/2027.42/138457.

MLA Handbook (7th Edition):

Barbarin, Andrea. “Toward Health Information Technology that Supports Overweight and Obese Women in Addressing Emotion- and Stress-Related Eating (a Mixed Methods Approach).” 2017. Web. 25 Nov 2020.

Vancouver:

Barbarin A. Toward Health Information Technology that Supports Overweight and Obese Women in Addressing Emotion- and Stress-Related Eating (a Mixed Methods Approach). [Internet] [Doctoral dissertation]. University of Michigan; 2017. [cited 2020 Nov 25]. Available from: http://hdl.handle.net/2027.42/138457.

Council of Science Editors:

Barbarin A. Toward Health Information Technology that Supports Overweight and Obese Women in Addressing Emotion- and Stress-Related Eating (a Mixed Methods Approach). [Doctoral Dissertation]. University of Michigan; 2017. Available from: http://hdl.handle.net/2027.42/138457

15. Basu, Sumanta. Modeling and Estimation of High-dimensional Vector Autoregressions.

Degree: PhD, Statistics, 2014, University of Michigan

 Vector Autoregression (VAR) represents a popular class of time series models in applied macroeconomics and finance, widely used for structural analysis and simultaneous forecasting of… (more)

Subjects/Keywords: High-dimensional Statistics; Time Series; Vector Autoregression; Granger Causality; Statistics and Numeric Data; Science

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APA (6th Edition):

Basu, S. (2014). Modeling and Estimation of High-dimensional Vector Autoregressions. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/109029

Chicago Manual of Style (16th Edition):

Basu, Sumanta. “Modeling and Estimation of High-dimensional Vector Autoregressions.” 2014. Doctoral Dissertation, University of Michigan. Accessed November 25, 2020. http://hdl.handle.net/2027.42/109029.

MLA Handbook (7th Edition):

Basu, Sumanta. “Modeling and Estimation of High-dimensional Vector Autoregressions.” 2014. Web. 25 Nov 2020.

Vancouver:

Basu S. Modeling and Estimation of High-dimensional Vector Autoregressions. [Internet] [Doctoral dissertation]. University of Michigan; 2014. [cited 2020 Nov 25]. Available from: http://hdl.handle.net/2027.42/109029.

Council of Science Editors:

Basu S. Modeling and Estimation of High-dimensional Vector Autoregressions. [Doctoral Dissertation]. University of Michigan; 2014. Available from: http://hdl.handle.net/2027.42/109029

16. Guo, Jian. Topics in High-Dimensional Unsupervised Learning.

Degree: PhD, Statistics, 2011, University of Michigan

 The first part of the dissertation introduces several new methods for estimating the structure of graphical models. Firstly, we consider estimating graphical models with discrete… (more)

Subjects/Keywords: Graphical Model; High-dimensinonal Data Analysis; Network Analysis; Unsupervised Learning; Statistics and Numeric Data; Science

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APA (6th Edition):

Guo, J. (2011). Topics in High-Dimensional Unsupervised Learning. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/86374

Chicago Manual of Style (16th Edition):

Guo, Jian. “Topics in High-Dimensional Unsupervised Learning.” 2011. Doctoral Dissertation, University of Michigan. Accessed November 25, 2020. http://hdl.handle.net/2027.42/86374.

MLA Handbook (7th Edition):

Guo, Jian. “Topics in High-Dimensional Unsupervised Learning.” 2011. Web. 25 Nov 2020.

Vancouver:

Guo J. Topics in High-Dimensional Unsupervised Learning. [Internet] [Doctoral dissertation]. University of Michigan; 2011. [cited 2020 Nov 25]. Available from: http://hdl.handle.net/2027.42/86374.

Council of Science Editors:

Guo J. Topics in High-Dimensional Unsupervised Learning. [Doctoral Dissertation]. University of Michigan; 2011. Available from: http://hdl.handle.net/2027.42/86374

17. Bellala, Gowtham. Information and Decision Theoretic Approaches to Problems in Active Diagnosis.

Degree: PhD, Electrical Engineering: Systems, 2012, University of Michigan

 In applications such as active learning or disease/fault diagnosis, one often encounters the problem of identifying an unknown object while minimizing the number of ``yes"… (more)

Subjects/Keywords: Machine Learning; Active Diagnosis; Active Learning; Object/Entity Identification; Bayesian Networks; Generalized Binary Search; Computer Science; Electrical Engineering; Engineering

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APA (6th Edition):

Bellala, G. (2012). Information and Decision Theoretic Approaches to Problems in Active Diagnosis. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/91606

Chicago Manual of Style (16th Edition):

Bellala, Gowtham. “Information and Decision Theoretic Approaches to Problems in Active Diagnosis.” 2012. Doctoral Dissertation, University of Michigan. Accessed November 25, 2020. http://hdl.handle.net/2027.42/91606.

MLA Handbook (7th Edition):

Bellala, Gowtham. “Information and Decision Theoretic Approaches to Problems in Active Diagnosis.” 2012. Web. 25 Nov 2020.

Vancouver:

Bellala G. Information and Decision Theoretic Approaches to Problems in Active Diagnosis. [Internet] [Doctoral dissertation]. University of Michigan; 2012. [cited 2020 Nov 25]. Available from: http://hdl.handle.net/2027.42/91606.

Council of Science Editors:

Bellala G. Information and Decision Theoretic Approaches to Problems in Active Diagnosis. [Doctoral Dissertation]. University of Michigan; 2012. Available from: http://hdl.handle.net/2027.42/91606

18. Jiang, Nan. A Theory of Model Selection in Reinforcement Learning.

Degree: PhD, Computer Science & Engineering, 2017, University of Michigan

 Reinforcement Learning (RL) is a machine learning paradigm where an agent learns to accomplish sequential decision-making tasks from experience. Applications of RL are found in… (more)

Subjects/Keywords: reinforcement learning; Computer Science; Engineering

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APA (6th Edition):

Jiang, N. (2017). A Theory of Model Selection in Reinforcement Learning. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/138518

Chicago Manual of Style (16th Edition):

Jiang, Nan. “A Theory of Model Selection in Reinforcement Learning.” 2017. Doctoral Dissertation, University of Michigan. Accessed November 25, 2020. http://hdl.handle.net/2027.42/138518.

MLA Handbook (7th Edition):

Jiang, Nan. “A Theory of Model Selection in Reinforcement Learning.” 2017. Web. 25 Nov 2020.

Vancouver:

Jiang N. A Theory of Model Selection in Reinforcement Learning. [Internet] [Doctoral dissertation]. University of Michigan; 2017. [cited 2020 Nov 25]. Available from: http://hdl.handle.net/2027.42/138518.

Council of Science Editors:

Jiang N. A Theory of Model Selection in Reinforcement Learning. [Doctoral Dissertation]. University of Michigan; 2017. Available from: http://hdl.handle.net/2027.42/138518

19. Tsiligkaridis, Theodoros. High Dimensional Separable Representations for Statistical Estimation and Controlled Sensing.

Degree: PhD, Electrical Engineering: Systems, 2014, University of Michigan

 This thesis makes contributions to a fundamental set of high dimensional problems in the following areas: (1) performance bounds for high dimensional estimation of structured… (more)

Subjects/Keywords: Separable Models for Covariance Estimation and Controlled Sensing; Convergence Theory for Decentralized Controlled Sensing; Collaborative Signal Processing; Electrical Engineering; Engineering

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APA (6th Edition):

Tsiligkaridis, T. (2014). High Dimensional Separable Representations for Statistical Estimation and Controlled Sensing. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/107110

Chicago Manual of Style (16th Edition):

Tsiligkaridis, Theodoros. “High Dimensional Separable Representations for Statistical Estimation and Controlled Sensing.” 2014. Doctoral Dissertation, University of Michigan. Accessed November 25, 2020. http://hdl.handle.net/2027.42/107110.

MLA Handbook (7th Edition):

Tsiligkaridis, Theodoros. “High Dimensional Separable Representations for Statistical Estimation and Controlled Sensing.” 2014. Web. 25 Nov 2020.

Vancouver:

Tsiligkaridis T. High Dimensional Separable Representations for Statistical Estimation and Controlled Sensing. [Internet] [Doctoral dissertation]. University of Michigan; 2014. [cited 2020 Nov 25]. Available from: http://hdl.handle.net/2027.42/107110.

Council of Science Editors:

Tsiligkaridis T. High Dimensional Separable Representations for Statistical Estimation and Controlled Sensing. [Doctoral Dissertation]. University of Michigan; 2014. Available from: http://hdl.handle.net/2027.42/107110

20. Schell, Greggory J. Personalized Medicine in Chronic Disease Management.

Degree: PhD, Industrial and Operations Engineering, 2015, University of Michigan

 Chronic diseases are persistent medical conditions which affect half of all adults in the United States. The nature of these long-term chronic conditions present monitoring… (more)

Subjects/Keywords: Operations research; Health care; Treatment planning; Chronic disease; Dynamic programming; Optimization; Industrial and Operations Engineering; Engineering

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APA (6th Edition):

Schell, G. J. (2015). Personalized Medicine in Chronic Disease Management. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/111447

Chicago Manual of Style (16th Edition):

Schell, Greggory J. “Personalized Medicine in Chronic Disease Management.” 2015. Doctoral Dissertation, University of Michigan. Accessed November 25, 2020. http://hdl.handle.net/2027.42/111447.

MLA Handbook (7th Edition):

Schell, Greggory J. “Personalized Medicine in Chronic Disease Management.” 2015. Web. 25 Nov 2020.

Vancouver:

Schell GJ. Personalized Medicine in Chronic Disease Management. [Internet] [Doctoral dissertation]. University of Michigan; 2015. [cited 2020 Nov 25]. Available from: http://hdl.handle.net/2027.42/111447.

Council of Science Editors:

Schell GJ. Personalized Medicine in Chronic Disease Management. [Doctoral Dissertation]. University of Michigan; 2015. Available from: http://hdl.handle.net/2027.42/111447

21. Singhal, Harsh. Statistical Inverse Problems on Graphs with Application to Flow Volume Estimation in Computer Networks.

Degree: PhD, Statistics, 2009, University of Michigan

 Estimation of flow volumes in computer networks involves the use of data that are either highly aggregated or fairly noisy. We address several conceptual and… (more)

Subjects/Keywords: Identifiability; Inverse Problem; Optimal Design; Statistics and Numeric Data; Science

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APA (6th Edition):

Singhal, H. (2009). Statistical Inverse Problems on Graphs with Application to Flow Volume Estimation in Computer Networks. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/62268

Chicago Manual of Style (16th Edition):

Singhal, Harsh. “Statistical Inverse Problems on Graphs with Application to Flow Volume Estimation in Computer Networks.” 2009. Doctoral Dissertation, University of Michigan. Accessed November 25, 2020. http://hdl.handle.net/2027.42/62268.

MLA Handbook (7th Edition):

Singhal, Harsh. “Statistical Inverse Problems on Graphs with Application to Flow Volume Estimation in Computer Networks.” 2009. Web. 25 Nov 2020.

Vancouver:

Singhal H. Statistical Inverse Problems on Graphs with Application to Flow Volume Estimation in Computer Networks. [Internet] [Doctoral dissertation]. University of Michigan; 2009. [cited 2020 Nov 25]. Available from: http://hdl.handle.net/2027.42/62268.

Council of Science Editors:

Singhal H. Statistical Inverse Problems on Graphs with Application to Flow Volume Estimation in Computer Networks. [Doctoral Dissertation]. University of Michigan; 2009. Available from: http://hdl.handle.net/2027.42/62268


University of Michigan

22. Qian, Min. Model Selection and l1 Penalization for Individualized Treatment Rules.

Degree: PhD, Statistics, 2010, University of Michigan

 Because many illnesses show heterogeneous response to treatment, there is increasing interest in individualizing treatment to patients. An individualized treatment rule is a decision rule… (more)

Subjects/Keywords: Individualized Treatment Rule; Decision Making; Model Selection; L1 Penalized Least Sqaures; Statistics and Numeric Data; Science

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APA (6th Edition):

Qian, M. (2010). Model Selection and l1 Penalization for Individualized Treatment Rules. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/77919

Chicago Manual of Style (16th Edition):

Qian, Min. “Model Selection and l1 Penalization for Individualized Treatment Rules.” 2010. Doctoral Dissertation, University of Michigan. Accessed November 25, 2020. http://hdl.handle.net/2027.42/77919.

MLA Handbook (7th Edition):

Qian, Min. “Model Selection and l1 Penalization for Individualized Treatment Rules.” 2010. Web. 25 Nov 2020.

Vancouver:

Qian M. Model Selection and l1 Penalization for Individualized Treatment Rules. [Internet] [Doctoral dissertation]. University of Michigan; 2010. [cited 2020 Nov 25]. Available from: http://hdl.handle.net/2027.42/77919.

Council of Science Editors:

Qian M. Model Selection and l1 Penalization for Individualized Treatment Rules. [Doctoral Dissertation]. University of Michigan; 2010. Available from: http://hdl.handle.net/2027.42/77919


University of Michigan

23. 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 November 25, 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. 25 Nov 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 Nov 25]. 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

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

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 November 25, 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. 25 Nov 2020.

Vancouver:

Zhang G. Extensions of the Penalized Spline Propensity Prediction Method of Imputation. [Internet] [Doctoral dissertation]. University of Michigan; 2007. [cited 2020 Nov 25]. 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

25. Shojaie, Ali. Estimation and Inference in High Dimensional Networks, with Applications to Biological Systems.

Degree: PhD, Statistics, 2010, University of Michigan

 This dissertation discusses several aspects of estimation and inference for high dimensional networks, and is divided into three main parts. First, to assess the significance… (more)

Subjects/Keywords: High Dimensional Networks; Graphical Models; Biological Networks and Systems Biology; Small N Large P Asymptotics; Penalized Likelihood Estimation; Bioinformatics; Mathematics; Statistics and Numeric Data; Health Sciences; Science

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APA (6th Edition):

Shojaie, A. (2010). Estimation and Inference in High Dimensional Networks, with Applications to Biological Systems. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/77775

Chicago Manual of Style (16th Edition):

Shojaie, Ali. “Estimation and Inference in High Dimensional Networks, with Applications to Biological Systems.” 2010. Doctoral Dissertation, University of Michigan. Accessed November 25, 2020. http://hdl.handle.net/2027.42/77775.

MLA Handbook (7th Edition):

Shojaie, Ali. “Estimation and Inference in High Dimensional Networks, with Applications to Biological Systems.” 2010. Web. 25 Nov 2020.

Vancouver:

Shojaie A. Estimation and Inference in High Dimensional Networks, with Applications to Biological Systems. [Internet] [Doctoral dissertation]. University of Michigan; 2010. [cited 2020 Nov 25]. Available from: http://hdl.handle.net/2027.42/77775.

Council of Science Editors:

Shojaie A. Estimation and Inference in High Dimensional Networks, with Applications to Biological Systems. [Doctoral Dissertation]. University of Michigan; 2010. Available from: http://hdl.handle.net/2027.42/77775

.