University of Illinois Urbana-Champaign

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

Gan, Lu

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Lu_Gan.pdf

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

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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