Multivariate generalized hidden Markov regression models with random covariates: Physical exercise in an elderly population

Statistics in Medicine
Antonio PunzoAntonello Maruotti


A time-varying latent variable model is proposed to jointly analyze multivariate mixed-support longitudinal data. The proposal can be viewed as an extension of hidden Markov regression models with fixed covariates (HMRMFCs), which is the state of the art for modelling longitudinal data, with a special focus on the underlying clustering structure. HMRMFCs are inadequate for applications in which a clustering structure can be identified in the distribution of the covariates, as the clustering is independent from the covariates distribution. Here, hidden Markov regression models with random covariates are introduced by explicitly specifying state-specific distributions for the covariates, with the aim of improving the recovering of the clusters in the data with respect to a fixed covariates paradigm. The hidden Markov regression models with random covariates class is defined focusing on the exponential family, in a generalized linear model framework. Model identifiability conditions are sketched, an expectation-maximization algorithm is outlined for parameter estimation, and various implementation and operational issues are discussed. Properties of the estimators of the regression coefficients, as well as of the hidden path parame...Continue Reading


Jan 26, 2013·Statistics in Medicine·Roland LangrockCiprian M Crainiceanu
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Mar 13, 2015·Biometrics·Jesse D Raffa, Joel A Dubin
Nov 3, 2016·Biometrical Journal. Biometrische Zeitschrift·Antonio Punzo, Paul D McNicholas
Jan 1, 2015·The Annals of Applied Statistics·John C JacksonZhiwei Zhang

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