Semiparametric Bayesian inference on generalized linear measurement error models
type: Item
instance of: scholarly article
title: Semiparametric Bayesian inference on generalized linear measurement error models
zbMATH Open document ID: 1383.62188
author: De-Wang Li
author: N. S. Tang
author: An Min Tang
published in: Statistical Papers
publication date: 2017-12-13T00:00:00Z
review text: Generalized linear models (GLMs) are widely used to fit responses that do not satisfy the usual requirements of least-squares methods. GLMs with covariates having measurement errors (MEs) are often referred to as generalized linear measurement error models (GLMEMs). This paper introduces GLMEMs by using a centered DP mixture model to specify the distribution of covarate MEs. It develops a Bayesian MCMC algorithm to make Bayesian inference on GLMEMs by using the Gibbs sampler together with the Metropolis-Hastings algorithm. Two Bayesian case deletion diagnostic measures are presented to detect influential observations based on the K-L divergence and Cook's distance.
reviewed by: Rózsa Horváth-Bokor
Mathematics Subject Classification ID: 62J12
Mathematics Subject Classification ID: 62F15
zbMATH DE Number: 6818217
zbMATH Keywords: Cook's distance
zbMATH Keywords: Dirichlet process prior
zbMATH Keywords: generalized linear models
zbMATH Keywords: Kullback-Leibler divergence
zbMATH Keywords: measurement error models
zbMATH Keywords: Bayesian inference
Wikidata QID: Q59518526
MaRDI profile type: Publication
full work available at URL: s00362-016-0739-x
OpenAlex ID: W2285491716
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DOI: 10.1007/S00362-016-0739-X
description: scientific article; zbMATH DE number 6818217