Prediction in regression models with continuous observations

dc.contributor.author	Dette, Holger
dc.contributor.author	Pepelyshev, Andrey
dc.contributor.author	Zhigljavsky, Anatoly
dc.date.accessioned	2019-08-30T14:16:22Z
dc.date.available	2019-08-30T14:16:22Z
dc.date.issued	2019
dc.description.abstract	We consider the problem of predicting values of a random process or ﬁeld satisfying a linear model y(x) = θ>f(x) + ε(x), where errors ε(x) are correlated. This is a common problem in kriging, where the case of discrete observations is standard. By focussing on the case of continuous observations, we derive expressions for the best linear unbiased predictors and their mean squared error. Our results are also applicable in the case where the derivatives of the process y are available, and either a response or one of its derivatives need to be predicted. The theoretical results are illustrated by several examples in particular for the popular Matérn 3/2 kernel.	en
dc.identifier.uri	http://hdl.handle.net/2003/38195
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-20174
dc.language.iso	en	de
dc.relation.ispartofseries	Discussion Paper / SFB823;15/2019
dc.subject	optimal prediction	en
dc.subject	best linear unbiased estimation	en
dc.subject	kriging	en
dc.subject	correlated observations	en
dc.subject.ddc	310
dc.subject.ddc	330
dc.subject.ddc	620
dc.title	Prediction in regression models with continuous observations	en
dc.type	Text	de
dc.type.publicationtype	workingPaper	de
dcterms.accessRights	open access
eldorado.secondarypublication	false	de

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