@techreport{21bad2df741f4808aba41b3dd9e79310,

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

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 = "Andrej Pazman",

year = "2005",

language = "English",

series = "Research Report Series / Department of Statistics and Mathematics",

number = "18",

publisher = "Institut f{\"u}r Statistik und Mathematik, WU Vienna University of Economics and Business",

edition = "June 2005",

type = "WorkingPaper",

institution = "Institut f{\"u}r Statistik und Mathematik, WU Vienna University of Economics and Business",

}