Semiparametric Bayesian inference for regression models

Yodit Seifu*, Thomas A. Severini, Martin A. Tanner

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

This paper presents a method for Bayesian inference for the regression parameters in a linear model with independent and identically distributed errors that does not require the specification of a parametric family of densities for the error distribution. This method first selects a nonparametric kernel density estimate of the error distribution which is unimodal and based on the least-squares residuals. Once the error distribution is selected, the Metropolis algorithm is used to obtain the marginal posterior distribution of the regression parameters. The methodology is illustrated with data sets, and its performance relative to standard Bayesian techniques is evaluated using simulation results.

Original languageEnglish (US)
Pages (from-to)719-734
Number of pages16
JournalCanadian Journal of Statistics
Volume27
Issue number4
DOIs
StatePublished - Dec 1999

Keywords

  • Bayesian inference
  • Kernel density estimate
  • Metropolis algorithm
  • Non-parametric
  • Regression parameters

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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