- AutorIn
- Prof. Dr.-Ing. Rüdiger Lehmann
- Titel
- Observation error model selection by information criteria vs. normality testing
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:520-qucosa-211721
- Quellenangabe
- Studia Geophysica et Geodaetica 59(2015)4, S. 489-504, DOI: 10.1007/s11200-015-0725-0
- Erstveröffentlichung
- 2015
- Abstract (EN)
- 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.
- Freie Schlagwörter (DE)
- Maximum-Likelihood-Schätzung, Robuste Schätzung, Gaußsche Normalverteilung, Laplace-Verteilung, Verallgemeinerte Normalverteilung, Kontaminierte Normalverteilung, Akaike's Informationskriterium, Anderson-Darling-Test, Monte-Carlo-Methode
- Freie Schlagwörter (EN)
- 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
- Klassifikation (DDC)
- 500
- Klassifikation (RVK)
- ZI 9070
- Herausgeber (Institution)
- Hochschule für Technik und Wirtschaft Dresden
- Publizierende Institution
- Hochschule für Technik und Wirtschaft Dresden, Dresden
- URN Qucosa
- urn:nbn:de:bsz:520-qucosa-211721
- Veröffentlichungsdatum Qucosa
- 17.10.2016
- Dokumenttyp
- Artikel
- Sprache des Dokumentes
- Englisch