General Kriging for Spatial-Temporal Processes with Random ARX-Regression Parameters

  • In the following, we discuss a procedure for interpolating a spatial-temporal stochastic process. We stick to a particular, moderately general model but the approach can be easily transered to other similar problems. The original data, which motivated this work, are measurements of gas concentrations (SO2, NO, O2) and several meteorological parameters (temperature, sun radiation, procipitation, wind speed etc.). These date have been and are still recorded twice every hour at several irregularly located places in the forests of the state Rheinland-Pfalz as part of a program monitoring the air pollution in the forests.

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Metadaten
Author:Jürgen Franke, B. Gründer
URN (permanent link):urn:nbn:de:hbz:386-kluedo-4929
Serie (Series number):Report in Wirtschaftsmathematik (WIMA Report) (7)
Document Type:Preprint
Language of publication:English
Year of Completion:1999
Year of Publication:1999
Publishing Institute:Technische Universität Kaiserslautern
Faculties / Organisational entities:Fachbereich Mathematik
DDC-Cassification:510 Mathematik

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