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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/21028

Title: Clusters with random size: maximum likelihood versus weighted estimation
Authors: Hermans, Lisa
Molenberghs, Geert
Kenward, M.G.
Van der Elst, Wim
Nassiri, Vahid
Aerts, Marc
Verbeke, Geert
Issue Date: 2015
Citation: Friedl, Herwig; Wagner, Helga (Ed.). Proceedings of the 30th International Workshop on Statistical Modelling, p. 215-220
Abstract: There are many contemporary designs that do not use a random sample of a fixed, a priori determined size. In case of informative cluster sizes, the cluster size is influenced by the the cluster’s data, but here we cope with some issues that even occur when the cluster size and the data are unrelated. First, fitting models to clusters of varying sizes is often more complicated than when all cluster have the same size. Secondly, in such cases, there usually is no so-called complete sufficient statistic (Molenberghs et al., 2014).
Notes: E-mail for correspondence: lisa.hermans@uhasselt.be
URI: http://hdl.handle.net/1942/21028
Category: C2
Type: Proceedings Paper
Appears in Collections: Research publications

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