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Varying coefficient model with errors in variables
Journal of the Korean Data & Information Science Society 2017;28:971-80
Published online September 30, 2017
© 2017 Korean Data & Information Science Society.

Insuk Sohn1 · Jooyong Shim2

1Statistics and Data Center, Samsung Medical Center
2Department of Statistics, Inje University
Correspondence to: Jooyong Shim
Adjunct Professor, Institute of Statistical Information, Department of Statistics, Inje University, Kimhae, 50834, Korea. E-mail: ds1631@hanmail.net
Received August 29, 2017; Revised September 11, 2017; Accepted September 13, 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 varying coefficient regression model has gained lots of attention since it is capable to model dynamic changes of regression coefficients in many regression problems of science. In this paper we propose a varying coefficient regression model that effectively considers the errors on both input and response variables, which utilizes the kernel method in estimating the varying coefficient which is the unknown nonlinear function of smoothing variables. We provide a generalized cross validation method for choosing the hyper-parameters which affect the performance of the proposed model. The proposed method is evaluated through numerical studies.
Keywords : Generalized cross validation function, kernel method, measurement error model, smoothing variable, varying coefficient regression model