Abstract
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.
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Lehmann, R. Observation error model selection by information criteria vs. normality testing. Stud Geophys Geod 59, 489–504 (2015). https://doi.org/10.1007/s11200-015-0725-0
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DOI: https://doi.org/10.1007/s11200-015-0725-0