A0758
Title: Mixture-based clustering with covariates for ordinal responses
Authors: Marta Nai Ruscone - Università degli Studi di Genova (Italy) [presenting]
Daniel Fernandez - Universitat Politecnica de Catalunya, BarcelonaTech (UPC) (Spain)
Kemmawadee Preedalikit - University of Phayao (Thailand)
Louise McMillan - Victoria University Wellington (New Zealand)
Ivy Liu - Victoria University Wellington (New Zealand)
Roy Costilla - AgResearch (New Zealand)
Abstract: Existing methods can perform likelihood-based clustering on a multivariate data matrix of ordinal responses, using finite mixtures to cluster the rows and columns of the matrix. Those models can incorporate the main effects of individual rows or columns and the cluster effects to model the matrix of responses. However, many real-world applications also include available covariates. Those mixture-based models are extended to include covariates and to examine what effect this has on the resulting clustering structures. The focus is on clustering the rows of the data matrix using the proportional odds version of the cumulative logit model for ordinal data. The models fit using the expectation-maximization (EM) algorithm, and their performance was assessed using a comprehensive simulation study. Finally, an application of the proposed models is also illustrated in the well-known arthritis clinical trial data set.
