Observation error model selection by information criteria vs. normality testing
| Autor: | Lehmann, Rüdiger |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
info:eu-repo/classification/ddc/500
ddc:500 Maximum-Likelihood-Schätzung Robuste Schätzung Gaußsche Normalverteilung Laplace-Verteilung Verallgemeinerte Normalverteilung Kontaminierte Normalverteilung Akaike's Informationskriterium Anderson-Darling-Test Monte-Carlo-Methode maximum likelihood estimation robust estimation Gaussian normal distribution Laplace distribution generalized normal distribution contaminated normal distribution Akaike information criterion Anderson Darling test Monte Carlo method |
| Zdroj: | Studia Geophysica et Geodaetica 59(2015)4, S. 489-504, DOI: 10.1007/s11200-015-0725-0 |
| Druh dokumentu: | Článek |
| Popis: | 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. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
| Externí odkaz: |
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