State space mixed models for binary responses with scale mixture of normal distributions links

Carlos A. Abanto-Valle and Dipak K. Dey

Computational Statistics & Data Analysis, 2014, vol. 71, issue C, 274-287

Abstract: A state space mixed models for binary time series where the inverse link function is modeled to be a cumulative distribution function of the scale mixture of normal (SMN) distributions. Specific inverse links examined include the normal, Student-t, slash and the variance gamma links. The threshold latent approach to represent the binary system as a linear state space model is considered. Using a Bayesian paradigm, an efficient Markov chain Monte Carlo (MCMC) algorithm is introduced for parameter estimation. The proposed methods are illustrated with real data sets. Empirical results showed that the slash inverse link fits better over the usual inverse probit link.

Keywords: Binary time series; Longitudinal data; Markov chain Monte Carlo; Particle learning; Probit; Scale mixture of normal links; Sequential Monte Carlo; State space models (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:71:y:2014:i:c:p:274-287

DOI: 10.1016/j.csda.2013.01.009

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