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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.

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Showing 1 to 2 of 2 (0.131 seconds)

Spelling suggestions: "subject:"akaike informationskriterium (AIC)"" "subject:"akaike informationskriteriums (AIC)""

1

Multiple Outlier Detection: Hypothesis Tests versus Model Selection by Information Criteria

Lehmann, Rüdiger, Lösler, Michael 14 June 2017 (has links) (PDF)
The detection of multiple outliers can be interpreted as a model selection problem. Models that can be selected are the null model, which indicates an outlier free set of observations, or a class of alternative models, which contain a set of additional bias parameters. A common way to select the right model is by using a statistical hypothesis test. In geodesy data snooping is most popular. Another approach arises from information theory. Here, the Akaike information criterion (AIC) is used to select an appropriate model for a given set of observations. The AIC is based on the Kullback-Leibler divergence, which describes the discrepancy between the model candidates. Both approaches are discussed and applied to test problems: the fitting of a straight line and a geodetic network. Some relationships between data snooping and information criteria are discussed. When compared, it turns out that the information criteria approach is more simple and elegant. Along with AIC there are many alternative information criteria for selecting different outliers, and it is not clear which one is optimal.
Methode der Kleinsten Quadrate
Ausreißererkennung
Hypothesentest
Informationskriterium
Akaike Informationskriterium (AIC)
Modellselektion
Least squares adjustment
Outlier detection
Hypothesis test
Information criterion
Akaike information criterion (AIC)
Data snooping
Model selection
ddc:526
rvk:ZI 9010
2

Multiple Outlier Detection: Hypothesis Tests versus Model Selection by Information Criteria

Lehmann, Rüdiger, Lösler, Michael January 2016 (has links)
The detection of multiple outliers can be interpreted as a model selection problem. Models that can be selected are the null model, which indicates an outlier free set of observations, or a class of alternative models, which contain a set of additional bias parameters. A common way to select the right model is by using a statistical hypothesis test. In geodesy data snooping is most popular. Another approach arises from information theory. Here, the Akaike information criterion (AIC) is used to select an appropriate model for a given set of observations. The AIC is based on the Kullback-Leibler divergence, which describes the discrepancy between the model candidates. Both approaches are discussed and applied to test problems: the fitting of a straight line and a geodetic network. Some relationships between data snooping and information criteria are discussed. When compared, it turns out that the information criteria approach is more simple and elegant. Along with AIC there are many alternative information criteria for selecting different outliers, and it is not clear which one is optimal.
info:eu-repo/classification/ddc/526
ddc:526

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