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Titel
Prediction in regression models with continuous observations / Holger Dette, Andrey Pepelyshev, Anatoly Zhigljavsky
VerfasserDette, Holger ; Pepelyshev, Andrey ; Žigljavskij, Anatolij Aleksandrovič
ErschienenDortmund : Universitätsbibliothek Dortmund, [August 2019]
Umfang1 Online-Ressource (29 Seiten) Diagramme
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Schlagwörteroptimal prediction / best linear unbiased estimation / kriging / correlated observations
Schlagwörter (GND)Regressionsanalyse / Kriging
URNurn:nbn:de:hbz:6:2-125706 
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We consider the problem of predicting values of a random process or field satisfying a linear model y(x) = > f(x) + "(x), where errors "(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.

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