Strong Consistency of Bayes Estimates in Stochastic Regression Models
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.fof i.i.d. random errors is assumed to have finite Fisher informationI=[integral operator][infinity]-[infinity](f')2/f dx<[infinity]; (2) for general priors, we assumefis strongly unimodal. The result can be considered as an application of a theorem of Doob to stochastic regression models.
Year of publication: |
1996
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Authors: | Hu, Inchi |
Published in: |
Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 57.1996, 2, p. 215-227
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Publisher: |
Elsevier |
Keywords: | Bayes estimates stochastic regressor martingale system identification adaptive control dynamic model strongly unimodal |
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