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Censored varying coecient regression model using Buckley-James method
Journal of the Korean Data & Information Science Society 2017;28:1167-77
Published online September 30, 2017
© 2017 Korean Data & Information Science Society.

Jooyong Shim1 · Kyungha Seok2

12Department of Statistics, Inje University
Correspondence to: Kyungha Seok
Professor, Institute of Statistical Information, Department of Statistics, Inje University, Kimhae 50834, Korea. E-mail: statskh@inje.ac.kr
Received July 18, 2017; Revised September 18, 2017; Accepted September 19, 2017.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
The censored regression using the pseudo-response variable proposed by Buckley and James has been one of the most well-known models. Recently, the varying coefficient regression model has received a great deal of attention as an important tool for modeling. In this paper we propose a censored varying coefficient regression model using Buckley-James method to consider situations where the regression coefficient of the model are not constant but change as the smoothing variables change. By using the formulation of least squares support vector machine (LS-SVM), the coefficient estimators of the proposed model can be easily obtained from simple linear equations. Furthermore, a generalized cross validation function can be easily derived. In this paper, we evaluated the proposed method and demonstrated the adequacy through simulate data sets and real data sets.
Keywords : Censored regression, generalized cross validation function, least squares support vector machine, pseudo-response variable, varying coefficient model