Bayesian prediction and model selection for locally asymptotically mixed normal models

Tomonari Sei, Fumiyasu Komaki

研究成果: Article査読

6 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)2523-2534
ページ数12
ジャーナルJournal of Statistical Planning and Inference
137
7
DOI
出版ステータスPublished - 2007 7月 1

ASJC Scopus subject areas

  • 統計学および確率
  • 統計学、確率および不確実性
  • 応用数学

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