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A copula formulation for multivariate latent Markov models
We specify a general formulation for multivariate latent Markov models for panel data, where outcomes are possibly of mixed-type (categorical,...
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Hidden Markov models for longitudinal rating data with dynamic response styles
This work deals with the analysis of longitudinal ordinal responses. The novelty of the proposed approach is in modeling simultaneously the temporal...
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Bayesian Latent Gaussian Models
Bayesian latent Gaussian models are Bayesian hierarchical models that assign Gaussian prior densities to the latent parameters. In this chapter, we...
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The Mixed Latent Markov Chain Model
A discussion of dynamic latent class analysis in a framework of the latent Markov chain model is made, following Chap. 5...
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Bayesian Hidden Markov Models for Early Warning
We show how Bayesian hidden Markov models may be employed to build early warning systems of particular risky events. The adopted model formulation...
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Variable Selection for Hidden Markov Models with Continuous Variables and Missing Data
We propose a variable selection method for multivariate hidden Markov models with continuous responses that are partially or completely missing at a...
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The Latent Markov Chain Model
The latent Markov chain model is discussed, and the relationship between the model and the latent class model is considered. An ML estimation...
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Latent Dirichlet Allocation and Hidden Markov Models to Identify Public Perception of Sustainability in Social Media Data
To help guide a just transition to a sustainable society and onboard the local communities, researchers can identify events of public interest...
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Regularized Latent Trajectory Models for Spatio-temporal Population Dynamics
Climate change impacts ecosystems variably in space and time. Landscape features may confer resistance against environmental stressors, whose...
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Markov switching stereotype logit models for longitudinal ordinal data affected by unobserved heterogeneity in responding behavior
When asked to assess their opinion about attitudes or perceptions on Likert-scale, respondents often endorse the midpoint or extremes of the scale...
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Tempered expectation-maximization algorithm for the estimation of discrete latent variable models
Maximum likelihood estimation of discrete latent variable (DLV) models is usually performed by the expectation-maximization (EM) algorithm. A...
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An expectation maximization algorithm for the hidden markov models with multiparameter student-t observations
Hidden Markov models are a class of probabilistic graphical models used to describe the evolution of a sequence of unknown variables from a set of...
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Matrix-Variate Hidden Markov Regression Models: Fixed and Random Covariates
Two families of matrix-variate hidden Markov regression models (MV-HMRMs) are here introduced. The distinction between them relies on the role of the...
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Path Analysis in Latent Class Models
The present chapter applies an entropy-based method of path analysis to multiple-indicator, multiple-cause models and the latent Markov chain model....
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Model-based two-way clustering of second-level units in ordinal multilevel latent Markov models
In this paper, an ordinal multilevel latent Markov model based on separate random effects is proposed. In detail, two distinct second-level discrete...
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Hidden Markov Models
This chapter introduces hidden Markov models (HMMs), which can be viewed as an extension of mixture models, in which a unit of observation (e.g., a...
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Identifiability of latent-variable and structural-equation models: from linear to nonlinear
An old problem in multivariate statistics is that linear Gaussian models are often unidentifiable. In factor analysis, an orthogonal rotation of the...
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hhsmm: an
R package for hidden hybrid Markov/semi-Markov modelsThis paper introduces the hhsmm
R package, which involves functions for initializing, fitting, and predication of hidden hybrid Markov/semi-Markov...
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Hidden Markov and Semi-Markov Models When and Why are These Models Useful for Classifying States in Time Series Data?
Hidden Markov models (HMMs) and their extensions have proven to be powerful tools for classification of observations that stem from systems with...
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An Introduction to Latent Class Analysis Methods and Applications
This book provides methods and applications of latent class analysis, and the following topics are taken up in the focus of discussion: basic latent...