Optimal designs for regression models with possible bias
type: Item
instance of: scholarly article
title: Optimal designs for regression models with possible bias
zbMATH Open document ID: 0669.62057
DOI: 10.1016/0378-3758(89)90063-3
author: William I. Notz
published in: Journal of Statistical Planning and Inference
publication date: 1989-01-01T00:00:00Z
review text: In regression problems, departures of the true regression function from the assumed model can render experimental designs which are optimal for the assumed model inefficient. If departures from the assumed model are possible, designs need to be modified so as to protect against bias introduced by fitting the wrong model. In some instances the experimenter may be able to specify what departures from the assumed model are likely and how likely these departures may be. Such information should be useful in selecting an experimental design. In this paper we investigate such situations by assuming that a prior distribution on possible departures from the assumed model is known. A general model is given and optimal designs are obtained for some polynomial models with certain types of prior distributions over the set of all polynomials.
Mathematics Subject Classification ID: 62K05
Mathematics Subject Classification ID: 62J05
zbMATH DE Number: 4096613
zbMATH Keywords: robustness
zbMATH Keywords: A-optimality
zbMATH Keywords: D-optimality
zbMATH Keywords: departures from the assumed model
zbMATH Keywords: bias
zbMATH Keywords: prior distribution
zbMATH Keywords: polynomial models
MaRDI profile type: Publication
full work available at URL: 0378-3758(89)90063-3
OpenAlex ID: W2066801212
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description: scientific article; zbMATH DE number 4096613