Likelihood-Based Finite Mixture Models for Ordinal Data

Conferenciante

Día y hora:

Jueves 27 de febrero de 2020 a las 12:00.

Lugar:

Auditorio del Centre de Recerca Matemàtica, Facultad de Ciencias, campus de la UAB. (enlace)

Durada:

1 hora aproximadamente

Inscripción:

La asistencia a este seminario es gratuita.
Por motivos de aforo os agradecemos que os registréis en el siguiente formulario: enlace

Abstract:

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.

Organizadores:

Grup de Recerca Advanced Stochastic Modelling

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