東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Inference in High Dimensional Regres...

Rakshit, Prabrisha.

Linked to FindBook

Google Book

Amazon

博客來

Inference in High Dimensional Regression.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Inference in High Dimensional Regression./
Author:	Rakshit, Prabrisha.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	153 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-05, Section: A.
Contained By:	Dissertations Abstracts International85-05A.
Subject:	Statistics. -
Online resource:	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.
LDR:02492nmm a2200385 4500 001 2396860
005 20240618081803.5
006 m o d
007 cr#unu||||||||
008 251215s2023 ||||||||||||||||| ||eng d
020 $a 9798380845694
035 $a (MiAaPQ)AAI30522565
035 $a AAI30522565
040 $a MiAaPQ $c MiAaPQ
100 1 $a Rakshit, Prabrisha. $3 3766614
245 1 0 $a Inference in High Dimensional Regression.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 153 p.
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.
790 $a 0190
791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30522565