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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/20861

Title: Fast and highly efficient pseudo-likelihood methodology for large and complex ordinal data
Authors: Ivanova, A.
Molenberghs, Geert
Verbeke, Geert
Issue Date: 2015
Citation: Statistical methods in medical research,p.1-27
Abstract: In longitudinal studies, continuous, binary, categorical, and survival outcomes are often jointly collected, possibly with some observations missing. However, when it comes to modeling responses, the ordinal ones have received less attention in the literature. In a longitudinal or hierarchical context, the univariate proportional odds mixed model (POMM) can be regarded as an instance of the generalized linear mixed model (GLMM). When the response of the joint multivariate model encompass ordinal responses, the complexity further increases. An additional problem of model fitting is the size of the collected data. Pseudo-likelihood based methods for pairwise fitting, for partitioned samples and, as introduced in this paper, pairwise fitting within partitioned samples allow joint modeling of even larger numbers of responses. We show that that pseudo-likelihood methodology allows for highly efficient and fast inferences in high-dimensional large datasets.
Notes: Corresponding author: Anna Ivanova, I-BioStat, KU Leuven, University of Leuven, Leuven, Belgium. Email: anna.ivanova@lstat.kuleuven.be
URI: http://hdl.handle.net/1942/20861
Link to publication: https://lirias.kuleuven.be/bitstream/123456789/515312/3/470.pdf
DOI: 10.1177/0962280215608213
ISSN: 0962-2802
Category: A1
Type: Journal Contribution
Validation: vabb, 2017
Appears in Collections: Research publications

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