Overview on Latent Markov Modeling
Introduction
Literature review on latent Markov models
Alternative approaches
Example datasets
Background on Latent Variable and Markov Chain Models
Introduction
Latent variable models
Expectation-Maximization algorithm
Standard errors
Latent class model
Selection of the number of latent classes
Applications
Markov chain model for longitudinal data
Applications
Basic Latent Markov Model
Introduction
Univariate formulation
Multivariate formulation
Model identifiability
Maximum likelihood estimation
Selection of the number of latent states
Applications
Constrained Latent Markov Models
Introduction
Constraints on the measurement model
Constraints on the latent model
Maximum likelihood estimation
Model selection and hypothesis testing
Applications
Including Individual Covariates and Relaxing Basic Model Assumptions
Introduction
Notation
Covariates in the measurement model
Covariates in the latent model
Interpretation of the resulting models
Maximum likelihood estimation
Observed information matrix, identifiability, and standard errors
Relaxing local independence
Higher order extensions
Applications
Including Random Effects and Extension to Multilevel Data
Introduction
Random-effects formulation
Maximum likelihood estimation
Multilevel formulation
Application to the student math achievement dataset
Advanced Topics about Latent Markov Modeling
Introduction
Dealing with continuous response variables
Dealing with missing responses
Additional computational issues
Decoding and forecasting
Selection of the number of latent states
Bayesian Latent Markov Models
Introduction
Prior distributions
Bayesian inference via reversible jump
Alternative sampling
Application to the labor market dataset
Appendix: Software
List of Main Symbols
Bibliography
Index