Support vector censored quantile regression under random censoring

Authors:
Jooyong Shim;Changha Hwang
Affiliations:
Department of Applied Statistics, Catholic University of Daegu, Kyungbuk 702-701, South Korea;Division of Information and Computer Science, Dankook University, Seoul 140-714, South Korea
Venue:
Computational Statistics & Data Analysis
Year:
2009

Citing 6
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Abstract

Censored quantile regression models have received a great deal of attention in both the theoretical and applied statistical literature. In this paper, we propose support vector censored quantile regression (SVCQR) under random censoring using iterative reweighted least squares (IRWLS) procedure based on the Newton method instead of usual quadratic programming algorithms. This procedure makes it possible to derive the generalized approximate cross validation (GACV) method for choosing the hyperparameters which affect the performance of SVCQR. Numerical results are then presented which illustrate the performance of SVCQR using the IRWLS procedure.