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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/312

Title: A Sensitivity Analysis of two Multivariate Response Models
Authors: Lesaffre, Emmanuel
Verbeke, Geert
Molenberghs, Geert
Keywords: Medical statistics
Issue Date: 1994
Citation: Computational Statistics and Data Analysis, 17(4), p. 363-391
Abstract: Using extensive Monte Carlo simulations, the sensitivity of two multivariate response models with respect to their underlying assumptions is investigated: the Multivariate Probit Model, already suggested in 1970 by Ashford and Sowden [4] and the Multivariate Global Cross Ratio Model, a generalization of Dale's model (see [7]). Both types of models are designed to regress a multivariate, ordered, categorical response vector on discrete and/or continuous measurements. This paper focuses on the behaviour of the maximum likelihood estimate of the association parameters mainly under misspecification of the marginal distributions. The investigation will be restricted to 2- and 3-dimensional response models. The use of the two models is illustrated on a medical and a biological data set.
URI: http://hdl.handle.net/1942/312
DOI: 10.1016/0167-9473(94)90018-3
ISI #: A1994NF78700002
ISSN: 0167-9473
Type: Journal Contribution
Appears in Collections: Research publications

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