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The Ohio State University

1. Yu, Lili. Variable selection in the general linear model for censored data.

Degree: PhD, Statistics, 2007, The Ohio State University

Variable selection is a popular topic in statistics today. However, for right censored data, only a few methods are available. The principle method assumes that the data comes from a Cox proportional hazards model. In 1997, Tibshirani proposed a variation of the LASSO method that minimizes the log partial likelihood subject to the sum of the absolute values of the parameters being bounded by a constant in the Cox proportional hazards model. Due to the nature of this constraint, it shrinks coefficients and produces some coefficients that are exactly zero. The resulting prediction error is smaller than that of subset selection methods. However, the proportional hazard assumption isn't always appropriate for real data. Therefore, we apply this method to the class of models (linear regression models) in which the response variable is right censored and the error is symmetric at zero, but is otherwise distribution free. The method also uses a sieve-likelihood to calculate a variation of the LASSO criterion and uses generalized cross-validation to choose the tuning parameter. Simulation shows that this method gives smaller prediction error than the method that depends on the proportional hazard assumption in some scenarios, especially for larger sample sizes. The performance of the proposed method is also examined via a data set from a study of the ganglioside content of primary brain tumors and a data set from a study of bone marrow transplants in Chronic Myelogenous Leukemia patients. Advisors/Committee Members: Pearl, Dennis (Advisor).

Subjects/Keywords: Statistics; LASSO; seive likelihood; model selection; right censored data

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Yu, L. (2007). Variable selection in the general linear model for censored data. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1173279515

Chicago Manual of Style (16th Edition):

Yu, Lili. “Variable selection in the general linear model for censored data.” 2007. Doctoral Dissertation, The Ohio State University. Accessed November 12, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1173279515.

MLA Handbook (7th Edition):

Yu, Lili. “Variable selection in the general linear model for censored data.” 2007. Web. 12 Nov 2019.

Vancouver:

Yu L. Variable selection in the general linear model for censored data. [Internet] [Doctoral dissertation]. The Ohio State University; 2007. [cited 2019 Nov 12]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1173279515.

Council of Science Editors:

Yu L. Variable selection in the general linear model for censored data. [Doctoral Dissertation]. The Ohio State University; 2007. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1173279515

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