We review the discrete latent variable approach, which is very popular in statistics and related fields. It allows us to formulate interpretable and flexible models that can be used to analyze complex datasets in the presence of articulated dependence structures among variables. Specific models including discrete latent variables are illustrated, such as finite mixture, latent class, hidden Markov, and stochastic block models. Algorithms for maximum likelihood and Bayesian estimation of these models are reviewed, focusing, in particular, on the expectation–maximization algorithm and the Markov chain Monte Carlo method with data augmentation. Model selection, particularly concerning the number of support points of the latent distribution, is discussed. The approach is illustrated by summarizing applications avail- able in the literature; a brief review of the main software packages to handle discrete latent variable models is also provided. Finally, some possible developments in this literature are suggested.
Bartolucci, F., Pandolfi, S., & Pennoni, F. (2022). Discrete Latent Variable Models. ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION, 9(1), 425-452 [10.1146/annurev-statistics-040220-091910].
Citazione: | Bartolucci, F., Pandolfi, S., & Pennoni, F. (2022). Discrete Latent Variable Models. ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION, 9(1), 425-452 [10.1146/annurev-statistics-040220-091910]. | |
Tipo: | Articolo in rivista - Articolo scientifico | |
Carattere della pubblicazione: | Scientifica | |
Presenza di un coautore afferente ad Istituzioni straniere: | No | |
Titolo: | Discrete Latent Variable Models | |
Autori: | Bartolucci, F; Pandolfi, S; Pennoni, F | |
Autori: | ||
Data di pubblicazione: | 2022 | |
Lingua: | English | |
Rivista: | ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION | |
Digital Object Identifier (DOI): | http://dx.doi.org/10.1146/annurev-statistics-040220-091910 | |
Appare nelle tipologie: | 01 - Articolo su rivista |