How to build your latent Markov model -- the role of time and space

TL;DR

This paper reviews the diverse landscape of latent Markov models, providing a unifying framework, practical guidance for model selection, and introducing the LaMa R package for efficient inference across various model classes.

Contribution

It offers a comprehensive unifying view of latent Markov models, guiding practitioners in model formulation and inference, and introduces the LaMa package for flexible, fast estimation.

Findings

Unified view of latent Markov models and related classes

Guidance on model selection based on data characteristics

Introduction of LaMa R package for efficient inference

Abstract

Statistical models that involve latent Markovian state processes have become immensely popular tools for analysing time series and other sequential data. However, the plethora of model formulations, the inconsistent use of terminology, and the various inferential approaches and software packages can be overwhelming to practitioners, especially when they are new to this area. With this review-like paper, we thus aim to provide guidance for both statisticians and practitioners working with latent Markov models by offering a unifying view on what otherwise are often considered separate model classes, from hidden Markov models over state-space models to Markov-modulated Poisson processes. In particular, we provide a roadmap for identifying a suitable latent Markov model formulation given the data to be analysed. Furthermore, we emphasise that it is key to applied work with any of these model classes to understand how recursive techniques exploiting the models' dependence structure can be used for inference. The R package LaMa adapts this unified view and provides an easy-to-use framework for very fast (C++ based) numerical maximum likelihood estimation of any of the models discussed in this paper, allowing users to tailor a latent Markov model to their data using a Lego-type approach.