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Likelihood-ratio tests for hidden Markov models.

P Giudici1, T Rydén, P Vandekerkhove

  • 1Department of Economics and Quantitative Methods, University of Pavia, Italy. giudici@unipv.it

Biometrics
|September 14, 2000
PubMed
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This study validates standard asymptotic theory for likelihood-ratio tests in hidden Markov models. These tests are essential for specifying multivariate Gaussian hidden Markov models, as demonstrated with real data.

Area of Science:

  • Statistics
  • Probability Theory

Background:

  • Hidden Markov models (HMMs) are powerful tools for modeling weakly dependent random phenomena.
  • Likelihood-ratio tests are fundamental for statistical inference and model selection.

Purpose of the Study:

  • To investigate the validity of standard asymptotic theory for likelihood-ratio tests applied to HMMs.
  • To demonstrate the practical application of these tests in specifying multivariate Gaussian HMMs.

Main Methods:

  • Theoretical analysis of likelihood-ratio test asymptotics under HMMs.
  • Application and illustration using multivariate Gaussian HMMs.
  • Empirical validation with a real-world dataset.

Main Results:

  • The standard asymptotic theory for likelihood-ratio tests is shown to be valid for HMMs under specific conditions.

Related Experiment Videos

  • The methodology provides a robust framework for model specification in Gaussian HMMs.
  • The approach is effective when applied to real observational data.

    • Conclusions:

    • Likelihood-ratio testing is a reliable statistical method for HMMs.
    • The findings facilitate the accurate specification and analysis of complex multivariate Gaussian HMMs.
    • The study confirms the practical utility of the developed methodology.