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Estimation and Conditional Inference...

Voorman, Arend L.

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Estimation and Conditional Inference in High-Dimensional Statistical Models.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Estimation and Conditional Inference in High-Dimensional Statistical Models./
Author:	Voorman, Arend L.
Description:	131 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-12(E), Section: B.
Contained By:	Dissertation Abstracts International75-12B(E).
Subject:	Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3631986
ISBN:	9781321111927

Estimation and Conditional Inference in High-Dimensional Statistical Models.
Voorman, Arend L.

Estimation and Conditional Inference in High-Dimensional Statistical Models. - 131 p.

Source: Dissertation Abstracts International, Volume: 75-12(E), Section: B.

Thesis (Ph.D.)--University of Washington, 2014.

In many areas of biology, recent advances in technology have facilitated the measurement of large numbers of features, while the number of observations in a data set may remain relatively modest. In this setting, lasso regression and related procedures have been extensively studied for prediction, while the problem of inference is relatively less studied. Most inference in high dimensions is based on simple marginal associations between variables. However, a richer characterization of the associations between variables can be obtained by examining conditional relationships, which account for the joint behavior of the variables. Inference on conditional relationships is more difficult, because it requires one to specify how features are related to one another, to estimate these relationships, and to characterize the uncertainty in the estimation procedure. In Chapters 2 and 3, we explore a few methods for testing hypotheses about conditional relationships in the high-dimensional setting. In Chapter 4, we note some strong distributional assumptions implicit in many treatments of high-dimensional graphical models, and propose a modification which treats this issue.

ISBN: 9781321111927Subjects--Topical Terms:

1002712
Biostatistics.

Estimation and Conditional Inference in High-Dimensional Statistical Models.
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245 1 0 $a Estimation and Conditional Inference in High-Dimensional Statistical Models.
300 $a 131 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-12(E), Section: B.
500 $a Advisers: Ali Shojaie; Daniela Witten.
502 $a Thesis (Ph.D.)--University of Washington, 2014.
520 $a In many areas of biology, recent advances in technology have facilitated the measurement of large numbers of features, while the number of observations in a data set may remain relatively modest. In this setting, lasso regression and related procedures have been extensively studied for prediction, while the problem of inference is relatively less studied. Most inference in high dimensions is based on simple marginal associations between variables. However, a richer characterization of the associations between variables can be obtained by examining conditional relationships, which account for the joint behavior of the variables. Inference on conditional relationships is more difficult, because it requires one to specify how features are related to one another, to estimate these relationships, and to characterize the uncertainty in the estimation procedure. In Chapters 2 and 3, we explore a few methods for testing hypotheses about conditional relationships in the high-dimensional setting. In Chapter 4, we note some strong distributional assumptions implicit in many treatments of high-dimensional graphical models, and propose a modification which treats this issue.
590 $a School code: 0250.
650 4 $a Biostatistics. $3 1002712
650 4 $a Statistics. $3 517247
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690 $a 0463
710 2 $a University of Washington. $b Biostatistics - Public Health. $3 3180015
773 0 $t Dissertation Abstracts International $g 75-12B(E).
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791 $a Ph.D.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3631986