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You searched for subject:(Statistics AND Numeric Data). Showing records 1 – 30 of 317 total matches.

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University of Michigan

1. Lee, Christopher. Improvements and Developments in Gene Regulation and Single-Cell Gene Expression Data Analysis.

Degree: PhD, Biostatistics, 2020, University of Michigan

 Recent advancements in high-throughput sequencing technologies have led to a vast amount of data assessing genome-wide regulation and single cell transcriptomics, which aid in the… (more)

Subjects/Keywords: Regulatory Genomics; Statistics and Numeric Data; Science

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APA (6th Edition):

Lee, C. (2020). Improvements and Developments in Gene Regulation and Single-Cell Gene Expression Data Analysis. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/155218

Chicago Manual of Style (16th Edition):

Lee, Christopher. “Improvements and Developments in Gene Regulation and Single-Cell Gene Expression Data Analysis.” 2020. Doctoral Dissertation, University of Michigan. Accessed September 20, 2020. http://hdl.handle.net/2027.42/155218.

MLA Handbook (7th Edition):

Lee, Christopher. “Improvements and Developments in Gene Regulation and Single-Cell Gene Expression Data Analysis.” 2020. Web. 20 Sep 2020.

Vancouver:

Lee C. Improvements and Developments in Gene Regulation and Single-Cell Gene Expression Data Analysis. [Internet] [Doctoral dissertation]. University of Michigan; 2020. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2027.42/155218.

Council of Science Editors:

Lee C. Improvements and Developments in Gene Regulation and Single-Cell Gene Expression Data Analysis. [Doctoral Dissertation]. University of Michigan; 2020. Available from: http://hdl.handle.net/2027.42/155218


University of Michigan

2. Zhou, Hao. Methods and Tools for Visual Analytics.

Degree: PhD, Statistics, 2011, University of Michigan

 Technological advances have led to a proliferation of data characterized by a complex structure; namely, high-dimensional attribute information complemented by relationships between the objects or… (more)

Subjects/Keywords: Visual Analytics; Statistics and Numeric Data; Science

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APA (6th Edition):

Zhou, H. (2011). Methods and Tools for Visual Analytics. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/89683

Chicago Manual of Style (16th Edition):

Zhou, Hao. “Methods and Tools for Visual Analytics.” 2011. Doctoral Dissertation, University of Michigan. Accessed September 20, 2020. http://hdl.handle.net/2027.42/89683.

MLA Handbook (7th Edition):

Zhou, Hao. “Methods and Tools for Visual Analytics.” 2011. Web. 20 Sep 2020.

Vancouver:

Zhou H. Methods and Tools for Visual Analytics. [Internet] [Doctoral dissertation]. University of Michigan; 2011. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2027.42/89683.

Council of Science Editors:

Zhou H. Methods and Tools for Visual Analytics. [Doctoral Dissertation]. University of Michigan; 2011. Available from: http://hdl.handle.net/2027.42/89683


University of Michigan

3. Qian, Cheng. Some Advances on Modeling High-Dimensional Data with Complex Structures.

Degree: PhD, Statistics, 2017, University of Michigan

 Recent advances in technology have created an abundance of high-dimensional data and made its analysis possible. These data require new, computationally efficient methodology and new… (more)

Subjects/Keywords: High-Dimensional; Statistics and Numeric Data; Science

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APA (6th Edition):

Qian, C. (2017). Some Advances on Modeling High-Dimensional Data with Complex Structures. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/140828

Chicago Manual of Style (16th Edition):

Qian, Cheng. “Some Advances on Modeling High-Dimensional Data with Complex Structures.” 2017. Doctoral Dissertation, University of Michigan. Accessed September 20, 2020. http://hdl.handle.net/2027.42/140828.

MLA Handbook (7th Edition):

Qian, Cheng. “Some Advances on Modeling High-Dimensional Data with Complex Structures.” 2017. Web. 20 Sep 2020.

Vancouver:

Qian C. Some Advances on Modeling High-Dimensional Data with Complex Structures. [Internet] [Doctoral dissertation]. University of Michigan; 2017. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2027.42/140828.

Council of Science Editors:

Qian C. Some Advances on Modeling High-Dimensional Data with Complex Structures. [Doctoral Dissertation]. University of Michigan; 2017. Available from: http://hdl.handle.net/2027.42/140828


University of Michigan

4. Babic, Boris. Foundations of Epistemic Risk.

Degree: PhD, Philosophy, 2017, University of Michigan

 My goal in this dissertation is to start a conversation about the role of risk in the decision-theoretic assessment of partial beliefs or credences in… (more)

Subjects/Keywords: Risk; Probability; Statistics; Epistemology; Philosophy; Statistics and Numeric Data; Humanities; Science

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APA (6th Edition):

Babic, B. (2017). Foundations of Epistemic Risk. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/140922

Chicago Manual of Style (16th Edition):

Babic, Boris. “Foundations of Epistemic Risk.” 2017. Doctoral Dissertation, University of Michigan. Accessed September 20, 2020. http://hdl.handle.net/2027.42/140922.

MLA Handbook (7th Edition):

Babic, Boris. “Foundations of Epistemic Risk.” 2017. Web. 20 Sep 2020.

Vancouver:

Babic B. Foundations of Epistemic Risk. [Internet] [Doctoral dissertation]. University of Michigan; 2017. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2027.42/140922.

Council of Science Editors:

Babic B. Foundations of Epistemic Risk. [Doctoral Dissertation]. University of Michigan; 2017. Available from: http://hdl.handle.net/2027.42/140922


University of Michigan

5. 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 September 20, 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. 20 Sep 2020.

Vancouver:

Yang Y. Robust Methods for Estimating the Mean with Missing Data. [Internet] [Doctoral dissertation]. University of Michigan; 2015. [cited 2020 Sep 20]. 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

6. Liu, Tzu-Ying. Outlier Detection for Mixed Model with Application to RNA-Seq Data.

Degree: PhD, Biostatistics, 2018, University of Michigan

 Extracting messenger RNA (mRNA) molecules using oligo-dT probes targeting on the Poly(A) tail is common in RNA-sequencing (RNA-seq) experiments. This approach, however, is limited when… (more)

Subjects/Keywords: Outlier detection; Mixed model; Statistics and Numeric Data; Science

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APA (6th Edition):

Liu, T. (2018). Outlier Detection for Mixed Model with Application to RNA-Seq Data. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/147613

Chicago Manual of Style (16th Edition):

Liu, Tzu-Ying. “Outlier Detection for Mixed Model with Application to RNA-Seq Data.” 2018. Doctoral Dissertation, University of Michigan. Accessed September 20, 2020. http://hdl.handle.net/2027.42/147613.

MLA Handbook (7th Edition):

Liu, Tzu-Ying. “Outlier Detection for Mixed Model with Application to RNA-Seq Data.” 2018. Web. 20 Sep 2020.

Vancouver:

Liu T. Outlier Detection for Mixed Model with Application to RNA-Seq Data. [Internet] [Doctoral dissertation]. University of Michigan; 2018. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2027.42/147613.

Council of Science Editors:

Liu T. Outlier Detection for Mixed Model with Application to RNA-Seq Data. [Doctoral Dissertation]. University of Michigan; 2018. Available from: http://hdl.handle.net/2027.42/147613


University of Michigan

7. Flickinger, Matthew. Detecting and Correcting Contamination in Genetic Data.

Degree: PhD, Biostatistics, 2016, University of Michigan

 While technological innovation has dramatically increased the amount and variety of genomic data available to geneticists, no assay is perfect and both human error and… (more)

Subjects/Keywords: contamination; genetic sequencing; Genetics; Statistics and Numeric Data; Science

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APA (6th Edition):

Flickinger, M. (2016). Detecting and Correcting Contamination in Genetic Data. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/120783

Chicago Manual of Style (16th Edition):

Flickinger, Matthew. “Detecting and Correcting Contamination in Genetic Data.” 2016. Doctoral Dissertation, University of Michigan. Accessed September 20, 2020. http://hdl.handle.net/2027.42/120783.

MLA Handbook (7th Edition):

Flickinger, Matthew. “Detecting and Correcting Contamination in Genetic Data.” 2016. Web. 20 Sep 2020.

Vancouver:

Flickinger M. Detecting and Correcting Contamination in Genetic Data. [Internet] [Doctoral dissertation]. University of Michigan; 2016. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2027.42/120783.

Council of Science Editors:

Flickinger M. Detecting and Correcting Contamination in Genetic Data. [Doctoral Dissertation]. University of Michigan; 2016. Available from: http://hdl.handle.net/2027.42/120783


University of Michigan

8. Quick, Corbin. Statistical and Computational Methods for Genome-Wide Association Analysis.

Degree: PhD, Biostatistics, 2018, University of Michigan

 Technological and scientific advances in recent years have revolutionized genomics. For example, decreases in whole genome sequencing (WGS) costs have enabled larger WGS studies as… (more)

Subjects/Keywords: statistical genetics; Public Health; Statistics and Numeric Data; Health Sciences; Science

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APA (6th Edition):

Quick, C. (2018). Statistical and Computational Methods for Genome-Wide Association Analysis. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/147697

Chicago Manual of Style (16th Edition):

Quick, Corbin. “Statistical and Computational Methods for Genome-Wide Association Analysis.” 2018. Doctoral Dissertation, University of Michigan. Accessed September 20, 2020. http://hdl.handle.net/2027.42/147697.

MLA Handbook (7th Edition):

Quick, Corbin. “Statistical and Computational Methods for Genome-Wide Association Analysis.” 2018. Web. 20 Sep 2020.

Vancouver:

Quick C. Statistical and Computational Methods for Genome-Wide Association Analysis. [Internet] [Doctoral dissertation]. University of Michigan; 2018. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2027.42/147697.

Council of Science Editors:

Quick C. Statistical and Computational Methods for Genome-Wide Association Analysis. [Doctoral Dissertation]. University of Michigan; 2018. Available from: http://hdl.handle.net/2027.42/147697


University of Michigan

9. Roy, Sandipan. Statistical Inference and Computational Methods for Large High-Dimensional Data with Network Structure.

Degree: PhD, Statistics, 2015, University of Michigan

 New technological advancements have allowed collection of datasets of large volume and different levels of complexity. Many of these datasets have an underlying network structure.… (more)

Subjects/Keywords: Network; Heterogeneous; High-dimernsional; Subsampling; Statistics and Numeric Data; Science

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APA (6th Edition):

Roy, S. (2015). Statistical Inference and Computational Methods for Large High-Dimensional Data with Network Structure. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/113602

Chicago Manual of Style (16th Edition):

Roy, Sandipan. “Statistical Inference and Computational Methods for Large High-Dimensional Data with Network Structure.” 2015. Doctoral Dissertation, University of Michigan. Accessed September 20, 2020. http://hdl.handle.net/2027.42/113602.

MLA Handbook (7th Edition):

Roy, Sandipan. “Statistical Inference and Computational Methods for Large High-Dimensional Data with Network Structure.” 2015. Web. 20 Sep 2020.

Vancouver:

Roy S. Statistical Inference and Computational Methods for Large High-Dimensional Data with Network Structure. [Internet] [Doctoral dissertation]. University of Michigan; 2015. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2027.42/113602.

Council of Science Editors:

Roy S. Statistical Inference and Computational Methods for Large High-Dimensional Data with Network Structure. [Doctoral Dissertation]. University of Michigan; 2015. Available from: http://hdl.handle.net/2027.42/113602


University of Michigan

10. Hosman, Carrie A. Methods to Control for Overt and Hidden Biases in Comparative Studies.

Degree: PhD, Statistics, 2011, University of Michigan

 When the goal of a comparative study is to ascertain the effect of some treatment condition, problems arise when it is not randomly assigned to… (more)

Subjects/Keywords: Comparative Studies; Propensity Scores; Prognostic Scores; Statistics and Numeric Data; Science

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APA (6th Edition):

Hosman, C. A. (2011). Methods to Control for Overt and Hidden Biases in Comparative Studies. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/86498

Chicago Manual of Style (16th Edition):

Hosman, Carrie A. “Methods to Control for Overt and Hidden Biases in Comparative Studies.” 2011. Doctoral Dissertation, University of Michigan. Accessed September 20, 2020. http://hdl.handle.net/2027.42/86498.

MLA Handbook (7th Edition):

Hosman, Carrie A. “Methods to Control for Overt and Hidden Biases in Comparative Studies.” 2011. Web. 20 Sep 2020.

Vancouver:

Hosman CA. Methods to Control for Overt and Hidden Biases in Comparative Studies. [Internet] [Doctoral dissertation]. University of Michigan; 2011. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2027.42/86498.

Council of Science Editors:

Hosman CA. Methods to Control for Overt and Hidden Biases in Comparative Studies. [Doctoral Dissertation]. University of Michigan; 2011. Available from: http://hdl.handle.net/2027.42/86498


University of Michigan

11. Zhu, Danting. Matching Methods for Estimating Effects of Time-dependent Treatment on Survival Outcomes.

Degree: PhD, Biostatistics, 2019, University of Michigan

 In observational studies, treatment is often evaluated through its impact on survival time. However, when treatment initiation is time-dependent, existing methods are either inapplicable or… (more)

Subjects/Keywords: Survival analysis; Matching; Statistics and Numeric Data; Science

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APA (6th Edition):

Zhu, D. (2019). Matching Methods for Estimating Effects of Time-dependent Treatment on Survival Outcomes. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/153369

Chicago Manual of Style (16th Edition):

Zhu, Danting. “Matching Methods for Estimating Effects of Time-dependent Treatment on Survival Outcomes.” 2019. Doctoral Dissertation, University of Michigan. Accessed September 20, 2020. http://hdl.handle.net/2027.42/153369.

MLA Handbook (7th Edition):

Zhu, Danting. “Matching Methods for Estimating Effects of Time-dependent Treatment on Survival Outcomes.” 2019. Web. 20 Sep 2020.

Vancouver:

Zhu D. Matching Methods for Estimating Effects of Time-dependent Treatment on Survival Outcomes. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2027.42/153369.

Council of Science Editors:

Zhu D. Matching Methods for Estimating Effects of Time-dependent Treatment on Survival Outcomes. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/153369


University of Michigan

12. Liu, Boang. Statistical Learning for Networks with Node Features.

Degree: PhD, Statistics, 2019, University of Michigan

 Network data represent connectivity relationships between individuals of interest and are common in many scientific fields, including biology, sociology, medicine and healthcare. Often, additional node… (more)

Subjects/Keywords: Statistical learning; Network analysis; Node features; Statistics and Numeric Data; Science

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APA (6th Edition):

Liu, B. (2019). Statistical Learning for Networks with Node Features. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/151579

Chicago Manual of Style (16th Edition):

Liu, Boang. “Statistical Learning for Networks with Node Features.” 2019. Doctoral Dissertation, University of Michigan. Accessed September 20, 2020. http://hdl.handle.net/2027.42/151579.

MLA Handbook (7th Edition):

Liu, Boang. “Statistical Learning for Networks with Node Features.” 2019. Web. 20 Sep 2020.

Vancouver:

Liu B. Statistical Learning for Networks with Node Features. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2027.42/151579.

Council of Science Editors:

Liu B. Statistical Learning for Networks with Node Features. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/151579


University of Michigan

13. Rashkin, Sara Rachel. Methods for Sequence Based Studies of Complex Traits.

Degree: PhD, Biostatistics, 2015, University of Michigan

 Thousands of loci have been associated with complex diseases and traits. However, there is still much we do not know about the biology of disease.… (more)

Subjects/Keywords: Statistical genetics; Genetics; Statistics and Numeric Data; Health Sciences; Science

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APA (6th Edition):

Rashkin, S. R. (2015). Methods for Sequence Based Studies of Complex Traits. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/116705

Chicago Manual of Style (16th Edition):

Rashkin, Sara Rachel. “Methods for Sequence Based Studies of Complex Traits.” 2015. Doctoral Dissertation, University of Michigan. Accessed September 20, 2020. http://hdl.handle.net/2027.42/116705.

MLA Handbook (7th Edition):

Rashkin, Sara Rachel. “Methods for Sequence Based Studies of Complex Traits.” 2015. Web. 20 Sep 2020.

Vancouver:

Rashkin SR. Methods for Sequence Based Studies of Complex Traits. [Internet] [Doctoral dissertation]. University of Michigan; 2015. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2027.42/116705.

Council of Science Editors:

Rashkin SR. Methods for Sequence Based Studies of Complex Traits. [Doctoral Dissertation]. University of Michigan; 2015. Available from: http://hdl.handle.net/2027.42/116705


University of Michigan

14. Dutta, Diptavo. Statistical Methods for Multiple Phenotypes and Gene-Set Association Analysis.

Degree: PhD, Biostatistics, 2019, University of Michigan

 As association studies continue to advance, more efficient statistical methods are required to fully utilize existing data and to provide insight into genetic architecture of… (more)

Subjects/Keywords: Multiple phenotypes; Gene-set; Kernel regression; Statistics and Numeric Data; Science

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APA (6th Edition):

Dutta, D. (2019). Statistical Methods for Multiple Phenotypes and Gene-Set Association Analysis. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/151689

Chicago Manual of Style (16th Edition):

Dutta, Diptavo. “Statistical Methods for Multiple Phenotypes and Gene-Set Association Analysis.” 2019. Doctoral Dissertation, University of Michigan. Accessed September 20, 2020. http://hdl.handle.net/2027.42/151689.

MLA Handbook (7th Edition):

Dutta, Diptavo. “Statistical Methods for Multiple Phenotypes and Gene-Set Association Analysis.” 2019. Web. 20 Sep 2020.

Vancouver:

Dutta D. Statistical Methods for Multiple Phenotypes and Gene-Set Association Analysis. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2027.42/151689.

Council of Science Editors:

Dutta D. Statistical Methods for Multiple Phenotypes and Gene-Set Association Analysis. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/151689


University of Michigan

15. Bagchi, Pramita. Non-Standard Statistical Inference Under Short and Long Range Dependence.

Degree: PhD, Statistics, 2015, University of Michigan

 The work discusses different non-standard problems under different types of short and long range dependence. In the first part we introduce new point-wise confidence interval… (more)

Subjects/Keywords: Inference under Dependence; Statistics and Numeric Data; Science

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APA (6th Edition):

Bagchi, P. (2015). Non-Standard Statistical Inference Under Short and Long Range Dependence. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/113564

Chicago Manual of Style (16th Edition):

Bagchi, Pramita. “Non-Standard Statistical Inference Under Short and Long Range Dependence.” 2015. Doctoral Dissertation, University of Michigan. Accessed September 20, 2020. http://hdl.handle.net/2027.42/113564.

MLA Handbook (7th Edition):

Bagchi, Pramita. “Non-Standard Statistical Inference Under Short and Long Range Dependence.” 2015. Web. 20 Sep 2020.

Vancouver:

Bagchi P. Non-Standard Statistical Inference Under Short and Long Range Dependence. [Internet] [Doctoral dissertation]. University of Michigan; 2015. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2027.42/113564.

Council of Science Editors:

Bagchi P. Non-Standard Statistical Inference Under Short and Long Range Dependence. [Doctoral Dissertation]. University of Michigan; 2015. Available from: http://hdl.handle.net/2027.42/113564


University of Michigan

16. Lu, Zhiyuan. Large Data Approaches to Thresholding Problems.

Degree: PhD, Statistics, 2019, University of Michigan

 Statistical models with discontinuities have seen much use in a variety of situations, in practical fields such as statistical process control, processing gene data, and… (more)

Subjects/Keywords: change point; adaptive sampling; computational time; Statistics and Numeric Data; Science

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APA (6th Edition):

Lu, Z. (2019). Large Data Approaches to Thresholding Problems. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/153384

Chicago Manual of Style (16th Edition):

Lu, Zhiyuan. “Large Data Approaches to Thresholding Problems.” 2019. Doctoral Dissertation, University of Michigan. Accessed September 20, 2020. http://hdl.handle.net/2027.42/153384.

MLA Handbook (7th Edition):

Lu, Zhiyuan. “Large Data Approaches to Thresholding Problems.” 2019. Web. 20 Sep 2020.

Vancouver:

Lu Z. Large Data Approaches to Thresholding Problems. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2027.42/153384.

Council of Science Editors:

Lu Z. Large Data Approaches to Thresholding Problems. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/153384


University of Michigan

17. Xu, Zhenzhen. Statistical Design and Survival Analysis in Cluster Randomized Trials.

Degree: PhD, Biostatistics, 2011, University of Michigan

 Cluster randomized trials, in which social units are selected as the units of randomization, have been increasingly used in the past three decades to evaluate… (more)

Subjects/Keywords: Statistical Design; Survival Analysis; Statistics and Numeric Data; Science

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APA (6th Edition):

Xu, Z. (2011). Statistical Design and Survival Analysis in Cluster Randomized Trials. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/84540

Chicago Manual of Style (16th Edition):

Xu, Zhenzhen. “Statistical Design and Survival Analysis in Cluster Randomized Trials.” 2011. Doctoral Dissertation, University of Michigan. Accessed September 20, 2020. http://hdl.handle.net/2027.42/84540.

MLA Handbook (7th Edition):

Xu, Zhenzhen. “Statistical Design and Survival Analysis in Cluster Randomized Trials.” 2011. Web. 20 Sep 2020.

Vancouver:

Xu Z. Statistical Design and Survival Analysis in Cluster Randomized Trials. [Internet] [Doctoral dissertation]. University of Michigan; 2011. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2027.42/84540.

Council of Science Editors:

Xu Z. Statistical Design and Survival Analysis in Cluster Randomized Trials. [Doctoral Dissertation]. University of Michigan; 2011. Available from: http://hdl.handle.net/2027.42/84540


University of Michigan

18. 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 September 20, 2020. http://hdl.handle.net/2027.42/84551.

MLA Handbook (7th Edition):

Laber, Eric B. “Adaptive Confidence Intervals for Non-Smooth Functionals.” 2011. Web. 20 Sep 2020.

Vancouver:

Laber EB. Adaptive Confidence Intervals for Non-Smooth Functionals. [Internet] [Doctoral dissertation]. University of Michigan; 2011. [cited 2020 Sep 20]. 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

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

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 September 20, 2020. http://hdl.handle.net/2027.42/133462.

MLA Handbook (7th Edition):

Wu, Tianshuang. “Set Valued Dynamic Treatment Regimes.” 2016. Web. 20 Sep 2020.

Vancouver:

Wu T. Set Valued Dynamic Treatment Regimes. [Internet] [Doctoral dissertation]. University of Michigan; 2016. [cited 2020 Sep 20]. 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

20. Gu, Yuqi. Statistical Analysis of Structured Latent Attribute Models.

Degree: PhD, Statistics, 2020, University of Michigan

 In modern psychological and biomedical research with diagnostic purposes, scientists often formulate the key task as inferring the fine-grained latent information under structural constraints. These… (more)

Subjects/Keywords: latent variable models; identifiability; psychometrics; Statistics and Numeric Data; Science

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APA (6th Edition):

Gu, Y. (2020). Statistical Analysis of Structured Latent Attribute Models. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/155196

Chicago Manual of Style (16th Edition):

Gu, Yuqi. “Statistical Analysis of Structured Latent Attribute Models.” 2020. Doctoral Dissertation, University of Michigan. Accessed September 20, 2020. http://hdl.handle.net/2027.42/155196.

MLA Handbook (7th Edition):

Gu, Yuqi. “Statistical Analysis of Structured Latent Attribute Models.” 2020. Web. 20 Sep 2020.

Vancouver:

Gu Y. Statistical Analysis of Structured Latent Attribute Models. [Internet] [Doctoral dissertation]. University of Michigan; 2020. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2027.42/155196.

Council of Science Editors:

Gu Y. Statistical Analysis of Structured Latent Attribute Models. [Doctoral Dissertation]. University of Michigan; 2020. Available from: http://hdl.handle.net/2027.42/155196


University of Michigan

21. Xia, Donggeng. Measuring Influence and Topic Dependent Interactions in Social Media Networks Based on a Counting Process Modeling Framework.

Degree: PhD, Statistics, 2015, University of Michigan

Data extracted from social media platforms, such as Twitter, are both large in scale and complex in nature, since they contain both unstructured text, as… (more)

Subjects/Keywords: User influence; Edge importance; Counting process; Statistics and Numeric Data; Science

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APA (6th Edition):

Xia, D. (2015). Measuring Influence and Topic Dependent Interactions in Social Media Networks Based on a Counting Process Modeling Framework. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/113379

Chicago Manual of Style (16th Edition):

Xia, Donggeng. “Measuring Influence and Topic Dependent Interactions in Social Media Networks Based on a Counting Process Modeling Framework.” 2015. Doctoral Dissertation, University of Michigan. Accessed September 20, 2020. http://hdl.handle.net/2027.42/113379.

MLA Handbook (7th Edition):

Xia, Donggeng. “Measuring Influence and Topic Dependent Interactions in Social Media Networks Based on a Counting Process Modeling Framework.” 2015. Web. 20 Sep 2020.

Vancouver:

Xia D. Measuring Influence and Topic Dependent Interactions in Social Media Networks Based on a Counting Process Modeling Framework. [Internet] [Doctoral dissertation]. University of Michigan; 2015. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2027.42/113379.

Council of Science Editors:

Xia D. Measuring Influence and Topic Dependent Interactions in Social Media Networks Based on a Counting Process Modeling Framework. [Doctoral Dissertation]. University of Michigan; 2015. Available from: http://hdl.handle.net/2027.42/113379


University of Michigan

22. Goldstick, Jason E. Contributions to Modeling the Dynamic Association Structure in Longitudinal Data Sets.

Degree: PhD, Statistics, 2010, University of Michigan

 This dissertation considers several modeling problems involving clustered longitudinal data. Interest focuses on the association structure rather than the means and, in particular, on its… (more)

Subjects/Keywords: Statistics; Statistics and Numeric Data; Science

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APA (6th Edition):

Goldstick, J. E. (2010). Contributions to Modeling the Dynamic Association Structure in Longitudinal Data Sets. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/77719

Chicago Manual of Style (16th Edition):

Goldstick, Jason E. “Contributions to Modeling the Dynamic Association Structure in Longitudinal Data Sets.” 2010. Doctoral Dissertation, University of Michigan. Accessed September 20, 2020. http://hdl.handle.net/2027.42/77719.

MLA Handbook (7th Edition):

Goldstick, Jason E. “Contributions to Modeling the Dynamic Association Structure in Longitudinal Data Sets.” 2010. Web. 20 Sep 2020.

Vancouver:

Goldstick JE. Contributions to Modeling the Dynamic Association Structure in Longitudinal Data Sets. [Internet] [Doctoral dissertation]. University of Michigan; 2010. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2027.42/77719.

Council of Science Editors:

Goldstick JE. Contributions to Modeling the Dynamic Association Structure in Longitudinal Data Sets. [Doctoral Dissertation]. University of Michigan; 2010. Available from: http://hdl.handle.net/2027.42/77719


University of Michigan

23. Kammeraad, Joshua. Digging Deeper into the Methods of Computational Chemistry.

Degree: PhD, Chemistry, 2020, University of Michigan

 This dissertation applies a skeptical but hopeful analytical paradigm and the tools of linear algebra, numerical methods, and machine learning to a diversity of problems… (more)

Subjects/Keywords: computational chemistry; data science; Computer Science; Chemistry; Mathematics; Statistics and Numeric Data; Engineering; Science

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APA (6th Edition):

Kammeraad, J. (2020). Digging Deeper into the Methods of Computational Chemistry. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/155137

Chicago Manual of Style (16th Edition):

Kammeraad, Joshua. “Digging Deeper into the Methods of Computational Chemistry.” 2020. Doctoral Dissertation, University of Michigan. Accessed September 20, 2020. http://hdl.handle.net/2027.42/155137.

MLA Handbook (7th Edition):

Kammeraad, Joshua. “Digging Deeper into the Methods of Computational Chemistry.” 2020. Web. 20 Sep 2020.

Vancouver:

Kammeraad J. Digging Deeper into the Methods of Computational Chemistry. [Internet] [Doctoral dissertation]. University of Michigan; 2020. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2027.42/155137.

Council of Science Editors:

Kammeraad J. Digging Deeper into the Methods of Computational Chemistry. [Doctoral Dissertation]. University of Michigan; 2020. Available from: http://hdl.handle.net/2027.42/155137


University of Michigan

24. Hunt, Gregory. Cell Type Deconvolution and Transformation of Microenvironment Microarray Data.

Degree: PhD, Statistics, 2018, University of Michigan

 Transformations are an important aspect of data analysis. In this work we explore the impact of data transformation on the analysis of high-throughput -omics data.… (more)

Subjects/Keywords: Cell Type Deconvolution and Transformation of Microenvironment Microarray Data; Statistics and Numeric Data; Science

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APA (6th Edition):

Hunt, G. (2018). Cell Type Deconvolution and Transformation of Microenvironment Microarray Data. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/147575

Chicago Manual of Style (16th Edition):

Hunt, Gregory. “Cell Type Deconvolution and Transformation of Microenvironment Microarray Data.” 2018. Doctoral Dissertation, University of Michigan. Accessed September 20, 2020. http://hdl.handle.net/2027.42/147575.

MLA Handbook (7th Edition):

Hunt, Gregory. “Cell Type Deconvolution and Transformation of Microenvironment Microarray Data.” 2018. Web. 20 Sep 2020.

Vancouver:

Hunt G. Cell Type Deconvolution and Transformation of Microenvironment Microarray Data. [Internet] [Doctoral dissertation]. University of Michigan; 2018. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2027.42/147575.

Council of Science Editors:

Hunt G. Cell Type Deconvolution and Transformation of Microenvironment Microarray Data. [Doctoral Dissertation]. University of Michigan; 2018. Available from: http://hdl.handle.net/2027.42/147575


University of Michigan

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

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 September 20, 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. 20 Sep 2020.

Vancouver:

Imbriano P. Methods for Improving Efficiency of Planned Missing Data Designs. [Internet] [Doctoral dissertation]. University of Michigan; 2018. [cited 2020 Sep 20]. 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


University of Michigan

26. Qiu, Sheng. Semiparametric and Joint Modeling of Cancer Screening.

Degree: PhD, Biostatistics, 2017, University of Michigan

 Introduction of screening for prostate cancer using the prostate-specific antigen (PSA) biomarker of the disease in the late 80ies led to remarkable dynamics of the… (more)

Subjects/Keywords: cancer screening; semiparametric modeling; time-to-event analysis; Statistics and Numeric Data; Science

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APA (6th Edition):

Qiu, S. (2017). Semiparametric and Joint Modeling of Cancer Screening. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/140805

Chicago Manual of Style (16th Edition):

Qiu, Sheng. “Semiparametric and Joint Modeling of Cancer Screening.” 2017. Doctoral Dissertation, University of Michigan. Accessed September 20, 2020. http://hdl.handle.net/2027.42/140805.

MLA Handbook (7th Edition):

Qiu, Sheng. “Semiparametric and Joint Modeling of Cancer Screening.” 2017. Web. 20 Sep 2020.

Vancouver:

Qiu S. Semiparametric and Joint Modeling of Cancer Screening. [Internet] [Doctoral dissertation]. University of Michigan; 2017. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2027.42/140805.

Council of Science Editors:

Qiu S. Semiparametric and Joint Modeling of Cancer Screening. [Doctoral Dissertation]. University of Michigan; 2017. Available from: http://hdl.handle.net/2027.42/140805


University of Michigan

27. Melipillan Araneda, Edmundo Roberto. Careless Survey Respondents: Approaches to Identify and Reduce their Negative Impact on Survey Estimates.

Degree: PhD, Survey Methodology, 2019, University of Michigan

 Multi-item response scales are widely used in surveys to assess a variety of constructs including respondents’ attitudes, behavior, and personality. Multi-item scales often appear in… (more)

Subjects/Keywords: Careless respondents; Satisficing; Person-fit indices; Autoencoder; Statistics and Numeric Data; Social Sciences

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APA (6th Edition):

Melipillan Araneda, E. R. (2019). Careless Survey Respondents: Approaches to Identify and Reduce their Negative Impact on Survey Estimates. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/153403

Chicago Manual of Style (16th Edition):

Melipillan Araneda, Edmundo Roberto. “Careless Survey Respondents: Approaches to Identify and Reduce their Negative Impact on Survey Estimates.” 2019. Doctoral Dissertation, University of Michigan. Accessed September 20, 2020. http://hdl.handle.net/2027.42/153403.

MLA Handbook (7th Edition):

Melipillan Araneda, Edmundo Roberto. “Careless Survey Respondents: Approaches to Identify and Reduce their Negative Impact on Survey Estimates.” 2019. Web. 20 Sep 2020.

Vancouver:

Melipillan Araneda ER. Careless Survey Respondents: Approaches to Identify and Reduce their Negative Impact on Survey Estimates. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2027.42/153403.

Council of Science Editors:

Melipillan Araneda ER. Careless Survey Respondents: Approaches to Identify and Reduce their Negative Impact on Survey Estimates. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/153403


University of Michigan

28. Jung, Young Hun. New Directions in Online Learning: Boosting, Partial Information, and Non-Stationarity.

Degree: PhD, Statistics, 2020, University of Michigan

 Online learning, where a learning algorithm fits a model on-the-fly with streaming data, has become an important research area in machine learning. Batch learning, where… (more)

Subjects/Keywords: Online Learning; Boosting; Partial Feedback; Restless Bandits; Thompson Sampling; Statistics and Numeric Data; Science

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

APA (6th Edition):

Jung, Y. H. (2020). New Directions in Online Learning: Boosting, Partial Information, and Non-Stationarity. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/155110

Chicago Manual of Style (16th Edition):

Jung, Young Hun. “New Directions in Online Learning: Boosting, Partial Information, and Non-Stationarity.” 2020. Doctoral Dissertation, University of Michigan. Accessed September 20, 2020. http://hdl.handle.net/2027.42/155110.

MLA Handbook (7th Edition):

Jung, Young Hun. “New Directions in Online Learning: Boosting, Partial Information, and Non-Stationarity.” 2020. Web. 20 Sep 2020.

Vancouver:

Jung YH. New Directions in Online Learning: Boosting, Partial Information, and Non-Stationarity. [Internet] [Doctoral dissertation]. University of Michigan; 2020. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2027.42/155110.

Council of Science Editors:

Jung YH. New Directions in Online Learning: Boosting, Partial Information, and Non-Stationarity. [Doctoral Dissertation]. University of Michigan; 2020. Available from: http://hdl.handle.net/2027.42/155110


University of Michigan

29. Liang, Qixing. Causal Inference in Health Science Research.

Degree: PhD, Biostatistics, 2019, University of Michigan

 Causal inference methods including propensity score (PS) matching and weighting have been widely used for comparative effectiveness research based on observational clinical databases. There are… (more)

Subjects/Keywords: causal inference; propensity score; inverse weighting; matching; augmentation; survival analysis; Statistics and Numeric Data; Science

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APA (6th Edition):

Liang, Q. (2019). Causal Inference in Health Science Research. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/153394

Chicago Manual of Style (16th Edition):

Liang, Qixing. “Causal Inference in Health Science Research.” 2019. Doctoral Dissertation, University of Michigan. Accessed September 20, 2020. http://hdl.handle.net/2027.42/153394.

MLA Handbook (7th Edition):

Liang, Qixing. “Causal Inference in Health Science Research.” 2019. Web. 20 Sep 2020.

Vancouver:

Liang Q. Causal Inference in Health Science Research. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2027.42/153394.

Council of Science Editors:

Liang Q. Causal Inference in Health Science Research. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/153394


University of Michigan

30. Tan, Yaoyuan Vincent. Novel Applications and Extensions for Bayesian Additive Regression Trees (BART) in Prediction, Imputation, and Causal Inference.

Degree: PhD, Biostatistics, 2018, University of Michigan

 The Bayesian additive regression trees (BART) is a method proposed by Chipman et al. (2010) that can handle non-linear main and multiple-way interaction effects for… (more)

Subjects/Keywords: Bayesian additive regression trees; Prediction; Imputation; Causal inference; Statistics and Numeric Data; Science

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

APA (6th Edition):

Tan, Y. V. (2018). Novel Applications and Extensions for Bayesian Additive Regression Trees (BART) in Prediction, Imputation, and Causal Inference. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/147594

Chicago Manual of Style (16th Edition):

Tan, Yaoyuan Vincent. “Novel Applications and Extensions for Bayesian Additive Regression Trees (BART) in Prediction, Imputation, and Causal Inference.” 2018. Doctoral Dissertation, University of Michigan. Accessed September 20, 2020. http://hdl.handle.net/2027.42/147594.

MLA Handbook (7th Edition):

Tan, Yaoyuan Vincent. “Novel Applications and Extensions for Bayesian Additive Regression Trees (BART) in Prediction, Imputation, and Causal Inference.” 2018. Web. 20 Sep 2020.

Vancouver:

Tan YV. Novel Applications and Extensions for Bayesian Additive Regression Trees (BART) in Prediction, Imputation, and Causal Inference. [Internet] [Doctoral dissertation]. University of Michigan; 2018. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2027.42/147594.

Council of Science Editors:

Tan YV. Novel Applications and Extensions for Bayesian Additive Regression Trees (BART) in Prediction, Imputation, and Causal Inference. [Doctoral Dissertation]. University of Michigan; 2018. Available from: http://hdl.handle.net/2027.42/147594

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