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 รf
โฆ LIBER โฆ
Quasi-Bayes averaging of stochastic approximation estimators
โ Scribed by E.A. Patrick; L.A. Liporace
- Book ID
- 114036843
- Publisher
- Elsevier Science
- Year
- 1971
- Weight
- 536 KB
- Volume
- 18
- Category
- Article
- ISSN
- 0019-9958
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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).