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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

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1173279515

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 (6^{th} 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 (16^{th} 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 (7^{th} 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