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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/442

Title: Calculating the appropriate information matrix for log-linear models when data are missing at random
Authors: Molenberghs, Geert
Kenward, Michael
Keywords: Mathematical Statistics
Longitudinal data
Missing data
Issue Date: 1997
Publisher: New York : Springer-Verlag
Citation: Gregoire, T., Brillinger, D.R., Diggle, P.J., Rusek-Cohen, E., Warren, W.G. & Wolfinger, R.D. (Ed.) Lecture Notes in Statistics 122, Proceedings of the Nantucket conference on Modelling Longitudinal and Spatially Correlated Data: Methods, Applications and Future Directions, New York : Springer-Verlag, p. 331-337
Abstract: It is commonly assumed that likelihood based inferences are valid when data are missing at random. In his original work on this topic, Rubin defined precisely the extent to which this statement holds. In particular, the observed but not the expected information matrix can be used for frequentist inference. In the rapidly growing literature on this subject, this fact is not always appreciated. An illustration is given, in the setting of the log-linear model for correlated binary data
URI: http://hdl.handle.net/1942/442
DOI: doi.org/10.1007/978-1-4612-0699-6_29
ISBN: 9780387982168
ISSN: 0930-0325
Type: Proceedings Paper
Appears in Collections: Research publications

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