✦ LIBER ✦

Strong Consistency of Bayes Estimates in Stochastic Regression Models

✍ Scribed by Inchi Hu

Publisher: Elsevier Science
Year: 1996
Tongue: English
Weight: 517 KB
Volume: 57
Category: Article
ISSN: 0047-259X
DOI: 10.1006/jmva.1996.0030

No coin nor oath required. For personal study only.

✦ Synopsis

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 Âf dx< ; (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.

📜 SIMILAR VOLUMES

Consistency of M-Estimates in General Re

Consistency of M-Estimates in General Regression Models

✍ F. Liese; I. Vajda 📂 Article 📅 1994 🏛 Elsevier Science 🌐 English ⚖ 669 KB

Strong Universal Pointwise Consistency o

Strong Universal Pointwise Consistency of Some Regression Function Estimates

✍ Paul Algoet; László Györfi 📂 Article 📅 1999 🏛 Elsevier Science 🌐 English ⚖ 159 KB

The strong universal pointwise consistency of some modified versions of the standard regression function estimates of partitioning, kernel, and nearest neighbor type is shown.

A New Class of Consistent Estimators for

A New Class of Consistent Estimators for Stochastic Linear Regressive Models

✍ Hong-Zhi An; Fred J Hickernell; Li-Xing Zhu 📂 Article 📅 1997 🏛 Elsevier Science 🌐 English ⚖ 349 KB

In this paper we propose a new approach for estimating the unknown parameter in the stochastic linear regressive model with stationary ergodic sequence of covariates. Under mild conditions on the joint distribution of the covariate and the error, the estimator constructed is shown to be strongly con

Comparing Stochastically Restricted Line

Comparing Stochastically Restricted Linear Estimators in a Regression Model

✍ Dr. Erkki P. Liski 📂 Article 📅 1989 🏛 John Wiley and Sons 🌐 English ⚖ 202 KB 👁 3 views

Conditions for superiority of the minimum dispersion estimator over another with respect to the covariance matrix are derived when the vector parameter of a regression model is subject to competing stochastic restrictions. The restrictions may also consist both of a deterministic part and a stochast

Comparing Stochastically Restricted Esti

Comparing Stochastically Restricted Estimators in a Linear Regression Model

✍ Dr. J. K. Baksalary 📂 Article 📅 1984 🏛 John Wiley and Sons 🌐 English ⚖ 150 KB 👁 3 views

Strong consistency of parameter estimate

Strong consistency of parameter estimates in direct self-tuning control algorithms based on stochastic approximation

✍ Miloje S. Radenković; Srdjan S. Stanković 📂 Article 📅 1990 🏛 Elsevier Science 🌐 English ⚖ 958 KB

Conditions for the strong consistency of the parameter estimates in direct self-tuning control algorithms based on stochastic approximation are derived in the general delay-general dither case and the influence of a priori information to the estimator dimension is analyzed.