Bayesian Analysis in Regression Models Using Pseudo-Likelihoods

DOI10.1080/03610920903277866zbMath1202.62040OpenAlexW2032372872MaRDI QIDQ3064083

Walter Racugno, Alessandra Salvan, Laura Ventura

Publication date: 20 December 2010

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610920903277866

zbMATH Keywords

asymptoticssemiparametric modelnuisance parameterprofile likelihoodestimating equationintegrated likelihood

Mathematics Subject Classification ID

Nonparametric estimation (62G05) Point estimation (62F10) Bayesian inference (62F15) General nonlinear regression (62J02)

Related Items (8)

A Matching Prior for the Shape Parameter of the Skew-Normal Distribution ⋮ Default Priors Based on Pseudo-Likelihoods for the Poisson-GPD Model ⋮ Pseudo-Likelihoods for Bayesian Inference ⋮ On interval and point estimators based on a penalization of the modified profile likelihood ⋮ Default prior distributions from quasi- and quasi-profile likelihoods ⋮ An RKHS framework for functional data analysis ⋮ Bayesian Additive Machine: classification with a semiparametric discriminant function ⋮ Higher-order Bayesian Approximations for Pseudo-posterior Distributions

Cites Work

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• Adjustments of the profile likelihood from a new perspective
• Negative binomial and mixed poisson regression
• Proper likelihoods for Bayesian analysis
• Likelihood functions for inference in the presence of a nuisance parameter
• Bayesian empirical likelihood
• Prior Distributions From Pseudo-Likelihoods in the Presence of Nuisance Parameters
• Applied Asymptotics
• Integrated likelihood functions for non-Bayesian inference

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