Paper Info
Title
A kernel-based parametric method for conditional density estimation
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 Nadaraya-Watson estimator through numerical simulation and practical data. Experimental results show that the proposed method outperforms the Nadaraya-Watson estimator in terms of revised mean integrated squared error (RMISE). Therefore, the proposed method is an effective method for estimating the conditional densities.
Year
DOI
Venue
2011
10.1016/j.patcog.2010.08.027
Pattern Recognition
Keywords
Field
DocType
conditional density,new kernel function,explanatory variable,exponential function,kernel function,conditional density function,conditional density estimation,kernel principal component analysis,nadaraya–watson estimator,effective method,unknown density,new kernel-based parametric method,nadaraya-watson estimator,density estimation,numerical simulation,mean integrated squared error
Applied mathematics,Conditional probability distribution,Kernel smoother,Kernel principal component analysis,Artificial intelligence,Kernel regression,Kernel density estimation,Multivariate kernel density estimation,Pattern recognition,Statistics,Variable kernel density estimation,Mathematics,Kernel (statistics)
Journal
Volume
Issue
ISSN
44
2
Pattern Recognition
Citations 
PageRank 
References 
6
0.77
6
Authors
3
Name
Order
Citations
PageRank
Gang Fu11196.13
Frank Y. Shih2110389.56
Haimin Wang3153.80