Extending finite mixture models with skew-normal distributions and hidden Markov models for time series
Nigri, Andrea Forti, Marco
Shang, Han Lin
Nigri, Andrea
Shang, Han Lin
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Abstract
We introduce an extension of finite mixture models by incorporating skew-normal distributions within a Hidden Markov Model framework. By assuming a constant transition probability matrix and allowing emission distributions to vary according to hidden states, the proposed model effectively captures dynamic dependencies between variables. Through the estimation of state-specific parameters, including location, scale, and skewness, the proposed model enables the detection of structural changes, such as shifts in the observed data distribution, while addressing challenges such as overfitting and computational inefficiencies inherent in Gaussian mixtures. Both simulation studies and real data analysis demonstrate the robustness and flexibility of the approach, highlighting its ability to accurately model asymmetric data and detect regime transitions.
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Date
2026
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Research Projects
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Keywords
Bayesian algorithm, Change point detection, Gender map in mortality, Regime transitions, Viterbi-type algorithm
Citation
Nigri, Andrea, Marco Forti, and Han Lin Shang. “Extending Finite Mixture Models with Skew-Normal Distributions and Hidden Markov Models for Time Series.” Journal of Statistical Computation and Simulation 96 (3): 563–90. 2026.