Authors: | Dette, Holger Pepelyshev, Andrey Zhigljavsky, Anatoly |
Title: | Prediction in regression models with continuous observations |
Language (ISO): | en |
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. |
Subject Headings: | optimal prediction best linear unbiased estimation kriging correlated observations |
URI: | http://hdl.handle.net/2003/38195 http://dx.doi.org/10.17877/DE290R-20174 |
Issue Date: | 2019 |
Appears in Collections: | Sonderforschungsbereich (SFB) 823 |
Files in This Item:
File | Description | Size | Format | |
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DP_1519_SFB823_Dette_Pepelyshev_Zhigljavsky.pdf | DNB | 604.94 kB | Adobe PDF | View/Open |
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