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.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:23301 |
| Date | January 2015 |
| Creators | Lehmann, Rüdiger |
| Publisher | Hochschule für Technik und Wirtschaft Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | doc-type:article, info:eu-repo/semantics/article, doc-type:Text |
| Source | Studia Geophysica et Geodaetica 59(2015)4, S. 489-504, DOI: 10.1007/s11200-015-0725-0 |
| Rights | info:eu-repo/semantics/openAccess |
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