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Strong consistency of Bayes estimates in nonlinear stochastic regression models

โœ Scribed by Inchi Hu

Publisher
Elsevier Science
Year
1998
Tongue
English
Weight
379 KB
Volume
67
Category
Article
ISSN
0378-3758

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โœฆ Synopsis

A broad range of nonlinear (linear) time series and stochastic processes can be described by the stochastic regression model y. = r.(O)+ e., where {en} are independent random disturbances and r. is a random function of an unknown parameter 0 measurable with respect to the a-field ~r(yl ..... y.-l). Here we establish the strong consistency of Bayes estimates in these stochastic regression models under a necessary and sufficient condition on the stochastic regressors, when prior distributions are discrete. This necessary and sufficient condition has been obtained previously by Wu (1981) for nonrandom r.. Herein we extend the result to nonlinear stochastic regression models using results from Shepp (1965) andShiryayev (1984). (~

๐Ÿ“œ SIMILAR VOLUMES

Strong Consistency of Bayes Estimates in
Strong Consistency of Bayes Estimates in Stochastic Regression Models
โœ Inchi Hu ๐Ÿ“‚ Article ๐Ÿ“… 1996 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 517 KB

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= & ( f $) 2 ร‚f