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Observation error model selection by information criteria vs. normality testing

To extract the best possible information from geodetic and geophysical observations, it is necessary to select a model of the observation errors, mostly the family of Gaussian normal distributions. However, there are alternatives, typically chosen in the framework of robust M-estimation. We give a synopsis of well-known and less well-known models for observation errors and propose to select a model based on information criteria. In this contribution we compare the Akaike information criterion (AIC) and the Anderson Darling (AD) test and apply them to the test problem of fitting a straight line. The comparison is facilitated by a Monte Carlo approach. It turns out that the model selection by AIC has some advantages over the AD test.

Identiferoai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:23301
Date January 2015
CreatorsLehmann, Rüdiger
PublisherHochschule für Technik und Wirtschaft Dresden
Source SetsHochschulschriftenserver (HSSS) der SLUB Dresden
LanguageEnglish
Detected LanguageEnglish
Typedoc-type:article, info:eu-repo/semantics/article, doc-type:Text
SourceStudia Geophysica et Geodaetica 59(2015)4, S. 489-504, DOI: 10.1007/s11200-015-0725-0
Rightsinfo:eu-repo/semantics/openAccess

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