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Row mixture-based clustering with covariates for ordinal responses.pdf (2.38 MB)

Row mixture-based clustering with covariates for ordinal responses

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posted on 2023-08-15, 21:25 authored by Kemmawadee Preedalikit, Daniel Fernández, I Ming LiuI Ming Liu, Louise McMillanLouise McMillan, Marta Nai Ruscone, Roy CostillaRoy Costilla

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 individual 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 individuals’ similar patterns of responses and the effects of the covariates on the individuals' 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.

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© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Publication date

2023-07-22

Project number

  • Non revenue

Language

  • English

Does this contain Māori information or data?

  • No

Publisher

Springer Nature

Journal title

Computational Statistics

ISSN

0943-4062

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