Tempered expectation-maximization algorithm for the estimation of discrete latent variable models

نویسندگان

چکیده

Abstract Maximum likelihood estimation of discrete latent variable (DLV) models is usually performed by the expectation-maximization (EM) algorithm. A well-known drawback related to multimodality log-likelihood function so that algorithm can converge a local maximum, not corresponding global one. We propose tempered EM explore parameter space adequately for two main classes DLV models, namely class and hidden Markov. compare proposal with standard an extensive Monte Carlo simulation study, evaluating both ability reach maximum computational time. show results analysis continuous cross-sectional longitudinal data referring some applications interest. All provide supporting evidence outperforms algorithm, it significantly improves chance maximum. The advantage relevant even considering overall computing

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ژورنال

عنوان ژورنال: Computational Statistics

سال: 2022

ISSN: ['0943-4062', '1613-9658']

DOI: https://doi.org/10.1007/s00180-022-01276-7