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Inference in High Dimensional Regression.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Inference in High Dimensional Regression./
作者:	Rakshit, Prabrisha.
面頁冊數:	1 online resource (153 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-05, Section: A.
Contained By:	Dissertations Abstracts International85-05A.
標題:	Statistics. -
電子資源:	click for full text (PQDT)
ISBN:	9798380845694

Inference in High Dimensional Regression.
Rakshit, Prabrisha.

Inference in High Dimensional Regression. - 1 online resource (153 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380845694Subjects--Topical Terms:

556824
Statistics.
Subjects--Index Terms:

Generalized linear modelsIndex Terms--Genre/Form:

554714
Electronic books.

Inference in High Dimensional Regression.
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504 $a Includes bibliographical references
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.
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538 $a Mode of access: World Wide Web
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653 $a Generalized linear models
653 $a High-dimensional regression
653 $a Bias-corrected estimator
653 $a Quadratic transformations
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