ORCID Profile
0000-0003-0012-2094
Current Organisations
Universitat Politècnica de Catalunya
,
Universitat Oberta de Catalunya
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Publisher: Wiley
Date: 30-09-2019
DOI: 10.1002/MPR.1801
Publisher: Springer Science and Business Media LLC
Date: 19-02-2019
Publisher: Springer Science and Business Media LLC
Date: 15-05-2018
Publisher: Informa UK Limited
Date: 09-10-2019
Publisher: Springer Science and Business Media LLC
Date: 22-07-2023
DOI: 10.1007/S00180-023-01387-9
Abstract: Existing methods can perform likelihood-based clustering on a multivariate data matrix of ordinal data, using finite mixtures to cluster the rows (observations) of the matrix. These models can incorporate the main effects of in idual rows and columns, as well as cluster effects, to model the matrix of responses. However, many real-world applications also include available covariates, which provide insights into the main characteristics of the clusters and determine clustering structures based on both the in iduals’ similar patterns of responses and the effects of the covariates on the in iduals' responses. In our research we have extended the mixture-based models to include covariates and test what effect this has on the resulting clustering structures. We focus on clustering the rows of the data matrix, using the proportional odds cumulative logit model for ordinal data. We fit the models using the Expectation-Maximization algorithm and assess performance using a simulation study. We also illustrate an application of the models to the well-known arthritis clinical trial data set.
No related grants have been discovered for Daniel Fernández.