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Classification with high-dimensional...

Huang, Jing.

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Classification with high-dimensional predictors and qualitative response. Applications to genetics.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Classification with high-dimensional predictors and qualitative response. Applications to genetics./
Author:	Huang, Jing.
Description:	91 p.
Notes:	Adviser: Richard A. Olshen.
Contained By:	Dissertation Abstracts International63-04B.
Subject:	Biology, Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3048543
ISBN:	0493628746

Classification with high-dimensional predictors and qualitative response. Applications to genetics.
Huang, Jing.

Classification with high-dimensional predictors and qualitative response. Applications to genetics. - 91 p.

Adviser: Richard A. Olshen.

Thesis (Ph.D.)--Stanford University, 2002.

In many real-world situations, multiple factors combine to determine outcome; those factors can have complicated and influential interactions, though perhaps small and insignificant individual contributions. One good example of such a situation is the association between polygenic disease and many genetic and environmental risk factors. Traditional approaches focusing on studying effects of individual factors have proven to be inadequate for such situations. My approach, on the contrary, does treat all risk factors together. It considers both interactions and complex combined effects simultaneously. The technique developed in this thesis stems from the traditional rooted binary classification tree method. It uses the binary tree as framework and employs penalized linear regression to define the specific rule for partitioning. Join classification tree and regression together not only allows us to consider combined effects and interactions simultaneously, but also provides us a simple, easy-to-interpret model. In addition, its predictive power and robustness are improved by the variable selection technique used in the binary split. This technique comprises several known basic components: cross-validation, bootstrapping, and permutation testing. The combination is novel. This algorithm has been applied to simulated data and also to data from a large scale genetic study. It demonstrates substantial improvement in performance over prior competitive binary tree-structured methodologies. Theoretical consistency of this method is also discussed.

ISBN: 0493628746Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Classification with high-dimensional predictors and qualitative response. Applications to genetics.
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100 1 $a Huang, Jing. $3 1036865
245 1 0 $a Classification with high-dimensional predictors and qualitative response. Applications to genetics.
300 $a 91 p.
500 $a Adviser: Richard A. Olshen.
500 $a Source: Dissertation Abstracts International, Volume: 63-04, Section: B, page: 1912.
502 $a Thesis (Ph.D.)--Stanford University, 2002.
520 $a In many real-world situations, multiple factors combine to determine outcome; those factors can have complicated and influential interactions, though perhaps small and insignificant individual contributions. One good example of such a situation is the association between polygenic disease and many genetic and environmental risk factors. Traditional approaches focusing on studying effects of individual factors have proven to be inadequate for such situations. My approach, on the contrary, does treat all risk factors together. It considers both interactions and complex combined effects simultaneously. The technique developed in this thesis stems from the traditional rooted binary classification tree method. It uses the binary tree as framework and employs penalized linear regression to define the specific rule for partitioning. Join classification tree and regression together not only allows us to consider combined effects and interactions simultaneously, but also provides us a simple, easy-to-interpret model. In addition, its predictive power and robustness are improved by the variable selection technique used in the binary split. This technique comprises several known basic components: cross-validation, bootstrapping, and permutation testing. The combination is novel. This algorithm has been applied to simulated data and also to data from a large scale genetic study. It demonstrates substantial improvement in performance over prior competitive binary tree-structured methodologies. Theoretical consistency of this method is also discussed.
590 $a School code: 0212.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Biology, Genetics. $3 1017730
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