Asymptotic optimality of experimental designs in estimating a product of means
type: Item
instance of: scholarly article
title: Asymptotic optimality of experimental designs in estimating a product of means
zbMATH Open document ID: 0697.62064
DOI: 10.1155/S104895339000003X
author: K. Rekab
published in: Journal of Applied Mathematics and Stochastic Analysis
publication date: 1990-01-01T00:00:00Z
full work available at URL: https://eudml.org/doc/46460
review text: Summary: In nonlinear estimation problems with linear models, one difficulty in obtaining optimal designs is their dependence on the true value of the unknown parameters. A Bayesian approach is adopted with the assumption the means are independent a priori and have conjugate prior distributions. The problem of designing an experiment to estimate the product of the means of two normal populations is considered. The main results determine an asymptotic lower bound for the Bayes risk, and a necessary and sufficient condition for any sequential procedure to achieve the bound.
Mathematics Subject Classification ID: 62K05
Mathematics Subject Classification ID: 62L05
Mathematics Subject Classification ID: 62C10
zbMATH DE Number: 4143277
zbMATH Keywords: nonlinear estimation
zbMATH Keywords: linear models
zbMATH Keywords: conjugate prior distributions
zbMATH Keywords: product of the means
zbMATH Keywords: normal populations
zbMATH Keywords: asymptotic lower bound for the Bayes risk
MaRDI profile type: Publication
description: scientific article; zbMATH DE number 4143277