A kernel-based parametric method for conditional density estimation

Document Type

Article

Publication Date

2-1-2011

Abstract

A conditional density function, which describes the relationship between response and explanatory variables, plays an important role in many analysis problems. In this paper, we propose a new kernel-based parametric method to estimate conditional density. An exponential function is employed to approximate the unknown density, and its parameters are computed from the given explanatory variable via a nonlinear mapping using kernel principal component analysis (KPCA). We develop a new kernel function, which is a variant to polynomial kernels, to be used in KPCA. The proposed method is compared with the NadarayaWatson estimator through numerical simulation and practical data. Experimental results show that the proposed method outperforms the NadarayaWatson estimator in terms of revised mean integrated squared error (RMISE). Therefore, the proposed method is an effective method for estimating the conditional densities. © 2010 Elsevier Ltd.

Identifier

77958000637 (Scopus)

Publication Title

Pattern Recognition

External Full Text Location

https://doi.org/10.1016/j.patcog.2010.08.027

ISSN

00313203

First Page

284

Last Page

294

Issue

2

Volume

44

Grant

ATM-0716950

Fund Ref

National Science Foundation

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