Likelihood-Based Finite Mixture Models for Ordinal Data
Daniel Fernández Martínez, Ph.D
Day and hour:
ThursdayFebruary 27th, 2020, at 12:00.
Auditorio del Centre de Recerca Matemàtica, Facultad de Ciencias, campus de la UAB. (link)
About 1 hour
Free registration at conference.
For capacity reasons will appreciate that you register in the following form: link
Many of the methods which deal with the reduction of dimensionality in matrices of data are based on mathematical techniques such as distance-based algorithms or matrix decomposition and eigenvalues. In general, it is not possible to use statistical inferences or select the appropriateness of a model via information criteria with these techniques because there is no underlying probability model. Additionally, the use of ordinal data is very common (e.g. Likert or pain scale). Recent research has developed a set of likelihood-based finite mixture models for a data matrix of ordinal data. This approach applies fuzzy clustering via finite mixtures to the stereotype model. Fuzzy allocation of rows, columns, and rows and columns simultaneously to corresponding clusters is obtained using unsupervised learning techniques (EM algorithm) and, also by Bayesian approaches (Reversible-Jump MCMC sampler). Examples with ordinal data sets will be shown to illustrate the application of this approach.
Grup de Recerca
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