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Inference in High Dimensional Regres...

Rakshit, Prabrisha.

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Inference in High Dimensional Regression.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Inference in High Dimensional Regression./
作者:	Rakshit, Prabrisha.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	153 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-05, Section: A.
Contained By:	Dissertations Abstracts International85-05A.
標題:	Statistics. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30522565
ISBN:	9798380845694

Inference in High Dimensional Regression.
Rakshit, Prabrisha.

Inference in High Dimensional Regression. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 153 p.

Source: Dissertations Abstracts International, Volume: 85-05, Section: A.

Thesis (Ph.D.)--Rutgers The State University of New Jersey, School of Graduate Studies, 2023.

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

This thesis proposes a novel statistical inference framework for high-dimensional generalized linear models (GLMs). The first project focuses on labeling patients in electronic health records as case or control using high-dimensional sparse logistic regression models. A lack of valid statistical inference methods for the case probability poses a major hurdle. To address this, the project proposes a novel bias-corrected estimator for the case probability and establishes its asymptotic normality. The second project considers high-dimensional sparse Poisson regression models and proposes bias-corrected estimators for linear and quadratic transformations of the high-dimensional regression vector. We apply the devised methodology to the high-dimensional mediation analysis, with a particular application of testing the interaction between the treatment variable and high-dimensional mediators. The third project presents the R package SIHR on statistical inferences in high-dimensional generalized linear models for continuous and binary outcomes. The package includes confidence interval construction and hypothesis testing for linear and quadratic functionals and demonstrates practical applications in both numerical examples and real data settings.

ISBN: 9798380845694Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Generalized linear models

Inference in High Dimensional Regression.
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500 $a Source: Dissertations Abstracts International, Volume: 85-05, Section: A.
500 $a Advisor: Guo, Zijian.
502 $a Thesis (Ph.D.)--Rutgers The State University of New Jersey, School of Graduate Studies, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a This thesis proposes a novel statistical inference framework for high-dimensional generalized linear models (GLMs). The first project focuses on labeling patients in electronic health records as case or control using high-dimensional sparse logistic regression models. A lack of valid statistical inference methods for the case probability poses a major hurdle. To address this, the project proposes a novel bias-corrected estimator for the case probability and establishes its asymptotic normality. The second project considers high-dimensional sparse Poisson regression models and proposes bias-corrected estimators for linear and quadratic transformations of the high-dimensional regression vector. We apply the devised methodology to the high-dimensional mediation analysis, with a particular application of testing the interaction between the treatment variable and high-dimensional mediators. The third project presents the R package SIHR on statistical inferences in high-dimensional generalized linear models for continuous and binary outcomes. The package includes confidence interval construction and hypothesis testing for linear and quadratic functionals and demonstrates practical applications in both numerical examples and real data settings.
590 $a School code: 0190.
650 4 $a Statistics. $3 517247
650 4 $a Biostatistics. $3 1002712
653 $a Generalized linear models
653 $a High-dimensional regression
653 $a Bias-corrected estimator
653 $a Quadratic transformations
690 $a 0463
690 $a 0501
690 $a 0308
710 2 $a Rutgers The State University of New Jersey, School of Graduate Studies. $b Statistics and Biostatistics. $3 3545045
773 0 $t Dissertations Abstracts International $g 85-05A.
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792 $a 2023
793 $a English
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