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Variable selection and statistical l...

Liu, Xiaoxi.

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Variable selection and statistical learning for censored data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Variable selection and statistical learning for censored data./
作者:	Liu, Xiaoxi.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2014,
面頁冊數:	137 p.
附註:	Source: Dissertation Abstracts International, Volume: 76-01(E), Section: B.
Contained By:	Dissertation Abstracts International76-01B(E).
標題:	Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3634028
ISBN:	9781321142242

Variable selection and statistical learning for censored data.
Liu, Xiaoxi.

Variable selection and statistical learning for censored data. - Ann Arbor : ProQuest Dissertations & Theses, 2014 - 137 p.

Source: Dissertation Abstracts International, Volume: 76-01(E), Section: B.

Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2014.

This dissertation focuses on (1) developing an efficient variable selection method for a class of general transformation models; (2) developing a support vector based method for predicting failure times allowing the coarsening at random assumption for the censoring distribution; (3) developing a statistical learning method for predicting recurrent events.

ISBN: 9781321142242Subjects--Topical Terms:

1002712
Biostatistics.

Variable selection and statistical learning for censored data.
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245 1 0 $a Variable selection and statistical learning for censored data.
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300 $a 137 p.
500 $a Source: Dissertation Abstracts International, Volume: 76-01(E), Section: B.
500 $a Adviser: Donglin Zeng.
502 $a Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2014.
520 $a This dissertation focuses on (1) developing an efficient variable selection method for a class of general transformation models; (2) developing a support vector based method for predicting failure times allowing the coarsening at random assumption for the censoring distribution; (3) developing a statistical learning method for predicting recurrent events.
520 $a In the first topic, we propose a computationally simple method for variable selection in a general class of transformation models with right-censored survival data. The proposed algorithm reduces to maximizing a weighted partial likelihood function within an adaptive lasso framework. We establish the asymptotic properties for the proposed method, including selection consistency and semiparametric efficiency of parameter estimators. We conduct simulation studies to investigate the small-sample performance. We apply the method to data sets from a primary biliary cirrhosis study and the Atherosclerosis Risk in Communities (ARIC) Study, and demonstrate its superior prediction performance as compared to existing risk scores.
520 $a In the second topic, we develop a novel support vector hazard regression approach for predicting survival outcomes. Our method adapts support vector machines to predict dichotomous outcomes of the counting processes among subjects at risk, and allows censoring times to depend on covariates without modeling the censoring distribution. The formulation can be solved conveniently using any convex quadratic programming package. Theoretically, we show that the decision rule is equivalent to maximizing the discrimination power based on hazard functions, and establish the consistency and learning rate of the predicted risk. Numerical experiments demonstrate a superior performance of the proposed method to existing learning methods. Real data examples from a study of Huntington's disease and the ARIC Study are used to illustrate the proposed method.
520 $a In the third topic, we adapt support vector machines in the context of the counting process to handle time-varying covariates and predict recurrent events. We conduct extensive simulation studies to compare performances of the proposed method to the Andersen and Gill proportional intensity model for the prediction of multiple recurrences. The extension of theoretical properties is described. We illustrate the proposed method by analyzing the data set from a bladder cancer study.
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