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Statistical Inference for Interactions and Infinite-Dimensional Estimands.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical Inference for Interactions and Infinite-Dimensional Estimands./
作者:	Hudson, Aaron.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	153 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Contained By:	Dissertations Abstracts International83-02B.
標題:	Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28547768
ISBN:	9798535507651

Statistical Inference for Interactions and Infinite-Dimensional Estimands.
Hudson, Aaron.

Statistical Inference for Interactions and Infinite-Dimensional Estimands. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 153 p.

Source: Dissertations Abstracts International, Volume: 83-02, Section: B.

Thesis (Ph.D.)--University of Washington, 2021.

This item must not be sold to any third party vendors.

In this dissertation, we make methodological contributions to three statistical inference problems. We first consider two challenges that can arise when assessing for interactions, and we then discuss inference for complex estimands in semiparametric and nonparametric models. In Chapter 2, we propose an inferential procedure for identifying when an effect is substantially stronger in some sub-populations than in others. In Chapter 3, we propose a method for differential network analysis in the setting where the dependencies shared among the nodes are associated with covariates. In Chapter 4, we introduce an approach to inference on infinite-dimensional estimands that does not require the estimand of interest to take a parametric form. We conclude with a summary and a discussion of potential future research directions in Chapter 5.

ISBN: 9798535507651Subjects--Topical Terms:

1002712
Biostatistics.
Subjects--Index Terms:

Differential Network Analysis

Statistical Inference for Interactions and Infinite-Dimensional Estimands.
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300 $a 153 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
500 $a Advisor: Shojaie, Ali.
502 $a Thesis (Ph.D.)--University of Washington, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a In this dissertation, we make methodological contributions to three statistical inference problems. We first consider two challenges that can arise when assessing for interactions, and we then discuss inference for complex estimands in semiparametric and nonparametric models. In Chapter 2, we propose an inferential procedure for identifying when an effect is substantially stronger in some sub-populations than in others. In Chapter 3, we propose a method for differential network analysis in the setting where the dependencies shared among the nodes are associated with covariates. In Chapter 4, we introduce an approach to inference on infinite-dimensional estimands that does not require the estimand of interest to take a parametric form. We conclude with a summary and a discussion of potential future research directions in Chapter 5.
590 $a School code: 0250.
650 4 $a Biostatistics. $3 1002712
650 4 $a Statistics. $3 517247
650 4 $a Statistical physics. $3 536281
650 4 $a Simulation. $3 644748
650 4 $a Population. $3 518693
650 4 $a Hypothesis testing. $3 3560946
650 4 $a Hypotheses. $3 3560118
650 4 $a Estimates. $3 3561047
650 4 $a Disease. $3 705846
650 4 $a Families & family life. $3 3422406
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650 4 $a Breast cancer. $3 3543523
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653 $a Differential Network Analysis
653 $a Effect Modification
653 $a Hypothesis Testing
653 $a Interactions
653 $a Nonparametric Inference
653 $a Semiparametric Inference
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793 $a English
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