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Elimination of less informative design points in regression models with a known or parametrized covariance function

Pazman, Andrej (2005) Elimination of less informative design points in regression models with a known or parametrized covariance function. Research Report Series / Department of Statistics and Mathematics, 18. Institut für Statistik und Mathematik, WU Vienna University of Economics and Business, Vienna.

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Abstract

We consider a regression model E[y(x)] = eta(theta, x) where x is a design point taken from a finite design space X. The covariance of observations is Cov[y(x), y(x*)] = C(x, x*, beta). Here, theta, beta are unknown vector parameters. The quality of the ML estimators of and is measured by optimality criteria applied on the Fisher information matrix taken at a fixed theta, beta (= local optimality). In this paper we give formulae to identify the design points which have little influence on this quality. We also propose a simple algorithm which is deleting such points and leads to a better (not necessarily optimum) design. (author's abstract)

Item Type: Paper
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Depositing User: Repository Administrator
Date Deposited: 21 Jun 2005 17:56
Last Modified: 28 Aug 2015 21:35
URI: http://epub.wu.ac.at/id/eprint/856

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