Initialization of Hidden Markov and Semi‐Markov Models: A Critical Evaluation of Several Strategies
From MaRDI portal
Publication:6088634
DOI10.1111/insr.12436OpenAlexW3124427131MaRDI QIDQ6088634
Antonello Maruotti, Antonio Punzo
Publication date: 14 December 2023
Published in: International Statistical Review (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1111/insr.12436
Applications of statistics (62Pxx) Inference from stochastic processes (62Mxx) Multivariate analysis (62Hxx)
Related Items (5)
Quantile hidden semi-Markov models for multivariate time series ⋮ Multivariate hidden semi-Markov models for longitudinal data: a dynamic regression modeling ⋮ Tempered expectation-maximization algorithm for the estimation of discrete latent variable models ⋮ A two-step estimator for multilevel latent class analysis with covariates ⋮ A two-step estimator for generalized linear models for longitudinal data with time-varying measurement error
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Generation of random clusters with specified degree of separation
- OCLUS: an analytic method for generating clusters with known overlap
- Initializing the EM algorithm in Gaussian mixture models with an unknown number of components
- Hidden Markov models with arbitrary state dwell-time distributions
- A multivariate hidden Markov model for the identification of sea regimes from incomplete skewed and circular time series
- Seven things to remember about hidden Markov models: A tutorial on Markovian models for time series
- Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models
- Choosing initial values for the EM algorithm for finite mixtures
- Separation index and partial membership for clustering
- hsmm -- an R package for analyzing hidden semi-Markov models
- Hidden semi-Markov models
- Hidden Markov models for alcoholism treatment trial data
- Stylized facts of financial time series and hidden semi-Markov models
- Constrained monotone EM algorithms for finite mixture of multivariate Gaussians
- Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges
- Model-based time-varying clustering of multivariate longitudinal data with covariates and outliers
- Selecting the number of states in hidden Markov models: pragmatic solutions illustrated using animal movement
- Dynamic mixtures of factor analyzers to characterize multivariate air pollutant exposures
- The influence of initial conditions on maximum likelihood estimation of the parameters of a binary hidden Markov model
- Efficient and effective learning of HMMs based on identification of hidden states
- Assessing the influence of marketing activities on customer behaviors: a dynamic clustering approach
- Initializing \(K\)-means batch clustering: A critical evaluation of several techniques
- Mixed Hidden Markov Models for Longitudinal Data: An Overview
- Latent Markov Models for Longitudinal Data
- Finding Groups in Data
- A Markov-switching generalized additive model for compound Poisson processes, with applications to operational loss models
- Numerical Maximisation of Likelihood: A Neglected Alternative to EM?
- Some applications of nonlinear and non-Gaussian state–space modelling by means of hidden Markov models
- A Multivariate Extension of the Dynamic Logit Model for Longitudinal Data Based on a Latent Markov Heterogeneity Structure
- A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains
- Univariate Discrete Distributions
- Hidden Markov Models for Time Series
- An experimental comparison of model-based clustering methods
- EM for mixtures
This page was built for publication: Initialization of Hidden Markov and Semi‐Markov Models: A Critical Evaluation of Several Strategies