Doubly regularized kernel regression with heteroscedastic censored data

Authors:
Jooyong Shim;Changha Hwang
Affiliations:
Department of Statistical Information, Catholic University of Daegu, Kyungbuk, Korea;Corresponding Author, Division of Information and Computer Sciences, Dankook University, Seoul, Korea
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Year:
2005

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Abstract

A doubly regularized likelihood estimating procedure is introduced for the heteroscedastic censored regression. The proposed procedure provides the estimates of both the conditional mean and the variance of the response variables, which are obtained by two stepwise iterative fashion. The generalized cross validation function and the generalized approximate cross validation function are used alternately to estimate tuning parameters in each step. Experimental results are then presented which indicate the performance of the proposed estimating procedure.