Bayesian prediction and model selection for locally asymptotically mixed normal models

Tomonari Sei, Fumiyasu Komaki

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

An information criterion for models with local asymptotic mixed normality (LAMN) is proposed. Since the widely known Akaike's Information Criterion (AIC) is derived on the basis of local asymptotic normality (LAN), it cannot be directly used to model selection of LAMN models, and so a criterion for these models is required. The proposed criterion for LAMN models is an asymptotically unbiased estimator of the Kullback-Leibler risk of Bayesian prediction. We present the results of simulation studies for a mixed normal model, a discretely observed diffusion model and a partially explosive Gaussian AR model.

Original languageEnglish
Pages (from-to)2523-2534
Number of pages12
JournalJournal of Statistical Planning and Inference
Volume137
Issue number7
DOIs
Publication statusPublished - 2007 Jul 1

Fingerprint

Bayesian Prediction
Bayesian Model
Model Selection
Normality
Local Asymptotic Normality
AR Model
Akaike Information Criterion
Model
Information Criterion
Unbiased estimator
Gaussian Model
Diffusion Model
Prediction
Model selection
Simulation Study

Keywords

  • Bayesian prediction
  • Information criterion
  • Kullback-Leibler divergence
  • Local asymptotic mixed normality
  • Model selection

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Applied Mathematics
  • Statistics and Probability

Cite this

Bayesian prediction and model selection for locally asymptotically mixed normal models. / Sei, Tomonari; Komaki, Fumiyasu.

In: Journal of Statistical Planning and Inference, Vol. 137, No. 7, 01.07.2007, p. 2523-2534.

Research output: Contribution to journalArticle

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