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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/5005

Title: Model selection for regression analyses with missing data
Authors: Aerts, Marc
Hens, Niel
Molenberghs, Geert
Issue Date: 2004
Publisher: Firenze University Press
Citation: Biggeri, Annibale; Dreassi, Emanuela; Lagazio, Corrado; Marchi, Marco (Ed.). Proceedings of the 19th International Workshop on Statistical Modelling. p. 43-47.
Abstract: The Akaiki Information Criterion, AIC, is one of the leading selection methods for regression models. In case of partially missing covariates with missingness probability depending on the response, regression estimates based on the so-called complete cases are known to be biased. In this contribution it is shown that model selection using AIC-values based on the complete cases can lead to the choice of wrong or less optimal models. In analogy with the weighted Horvitz-Thompson estimator, we propose a weighted version of AIC. It is shown that this weighted AIC criterion improves model choices.
URI: http://hdl.handle.net/1942/5005
Link to publication: http://www.statmod.org/files/proceedings/iwsm2004_proceedings.pdf
ISBN: 8884531934
Category: C2
Type: Proceedings Paper
Appears in Collections: Research publications

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