Strong consistency of Bayes estimates in stochastic regression models

JOURNAL OF MULTIVARIATE ANALYSIS

卷:57

Article

关键词: Bayes estimates;    stochastic regressor;    martingale;    system identification;    adaptive control;    dynamic model;    strongly unimodal;   

DOI  :  10.1006/jmva.1996.0030

来源: Elsevier

【 摘 要 】

Under minimum assumptions on the stochastic regressors, strong consistency of Bayes estimates is established in stochastic regression models in two cases: (1) When the prior distribution is discrete, the p.d.f. f of i.i.d. random errors is assumed to have finite Fisher information I=integral(-infinity)(infinity) (f')(2)/f dx < infinity; (2) for general priors, we assume f is strongly unimodal. The result can be considered as an application of a theorem of Doob to stochastic regression models. (C) 1996 Academic Press. Inc.

【 授权许可】

Free