[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.7465/jkdi.2012.23.6.1231

Kernel Poisson regression for mixed input variables

Shim, Jooyong (Department of Data Science, Inje University)

Publication Information

Journal of the Korean Data and Information Science Society / v.23, no.6, 2012 , pp. 1231-1239 More about this Journal

Abstract

An estimating procedure is introduced for kernel Poisson regression when the input variables consist of numerical and categorical variables, which is based on the penalized negative log-likelihood and the component-wise product of two different types of kernel functions. The proposed procedure provides the estimates of the mean function of the response variables, where the canonical parameter is linearly and/or nonlinearly related to the input variables. Experimental results are then presented which indicate the performance of the proposed kernel Poisson regression.

Keywords

Canonical parameter; kernel function; partially linear regression; penalized negative log-likelihood; Poisson regression;

Citations & Related Records

Times Cited By KSCI : 2 (Citation Analysis)

Reference
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