Variable screening and model selection in censored quantile regression via sparse penalties and stepwise refinement

Gan, Lu

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https://hdl.handle.net/2142/49692 Copy

Description

Title

Variable screening and model selection in censored quantile regression via sparse penalties and stepwise refinement

Author(s)

Gan, Lu

Issue Date

2014-05-30T17:05:07Z

Doctoral Committee Chair(s)

• Portnoy, Stephen L.
• Simpson, Douglas G.

Committee Member(s)

• Koenker, Roger W.
• Liang, Feng

Department of Study

Statistics

Discipline

Statistics

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Date of Ingest

2014-05-30T17:05:07Z

Keyword(s)

• Variable Screening
• Censored Data
• Quantile Regression
• Least Absolute Selection and Shrinkage Operator (LASSO)
• Smoothly Clipped Absolute Deviation (SCAD)
• Portnoy
• Peng and Huang
• Stepwise Regression
• Bidirectional
• Backward
• Left Censoring
• Random Censoring

Abstract

Many variable selection methods are available for linear regression but very little has been developed for quantile regression, especially for the censored problems. This study will look at the possibilities of utilizing some existing penalty variable selection methods on censored quantile regression problems. In the situation when censored values are not known for each observation, it is common to model the censoring as random. Under the assumption that y_i and C_i are conditionally independent given x_i, we use the random censored quantile regression Portnoy estimators (2010). This method simplifies the censored problem into a weight problem. When combined with the penalized regression method: LASSO and SCAD, one can perform variable screening for the censored data at quantiles of interest. Furthermore, we establish the asymptotic property, and illustrate the methodology in the context of ultrasound safety study.

Graduation Semester

2014-05

Permalink

http://hdl.handle.net/2142/49692

Copyright and License Information

Copyright 2014 Lu Gan

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