Support vector censored quantile regression under random censoring
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.
Year of publication: |
2009
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Authors: | Shim, Jooyong ; Hwang, Changha |
Published in: |
Computational Statistics & Data Analysis. - Elsevier, ISSN 0167-9473. - Vol. 53.2009, 4, p. 912-919
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Publisher: |
Elsevier |
Saved in:
Online Resource
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