Bayesian approach to bandwidth selection for multivariate count regression function estimation by associated discrete kernel

被引：3

作者：

Djerroud L. ^{[1
]}

Zougab N. ^{[1
,2
]}

Adjabi S. ^{[1
]}

机构：

[1] Laboratory LAMOS, University of Bejaia, Bejaia, Targa Ouzermour

[2] Department of Mathematics, Faculty of Sciences, University of Tizi-Ouzou, Tizi-Ouzou

来源：

Journal of Statistical Theory and Practice | 2017年 / 11卷 / 4期

关键词：

Bayesian approach; cross-validation; discrete kernel; multivariate kernel regression;

D O I：

10.1080/15598608.2017.1281180

中图分类号：

学科分类号：

摘要：

Nonparametric regression is an important tool for exploring the unknown relationship between a response variable and a set of explanatory variables also known as regressors. This article introduces the associated discrete kernel for multivariate nonparametric count regression estimation. We propose a Bayesian approach based upon likelihood cross-validation and a Monte Carlo Markov chain (MCMC) method for deriving the global optimal bandwidths. Through simulation and real count data, we point out the performance of binomial and triangular discrete kernels. A comparative study of the Bayesian approach and cross-validation technique is also presented. © 2017 Grace Scientific Publishing, LLC.

引用

页码：553 / 572

页数：19

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