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

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  • Abanto-Valle, Carlos A.
  • Dey, Dipak K.

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.

Suggested Citation

  • Abanto-Valle, Carlos A. & Dey, Dipak K., 2014. "State space mixed models for binary responses with scale mixture of normal distributions links," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 274-287.
    • Handle: RePEc:eee:csdana:v:71:y:2014:i:c:p:274-287
    DOI: 10.1016/j.csda.2013.01.009
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    References listed on IDEAS

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    Cited by:

    1. Chénangnon Frédéric Tovissodé & Aliou Diop & Romain Glèlè Kakaï, 2021. "Inference in skew generalized t-link models for clustered binary outcome via a parameter-expanded EM algorithm," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-31, April.
    2. Dimitrakopoulos, Stefanos & Dey, Dipak K., 2017. "Discrete-response state space models with conditional heteroscedasticity: An application to forecasting the federal funds rate target," Economics Letters, Elsevier, vol. 154(C), pages 20-23.
    3. Wang, Min & Yang, Mingan, 2016. "Posterior property of Student-t linear regression model using objective priors," Statistics & Probability Letters, Elsevier, vol. 113(C), pages 23-29.
    4. Yang Lu, 2020. "A simple parameter‐driven binary time series model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 187-199, March.
    5. Shuaimin Kang & Guangying Liu & Howard Qi & Min Wang, 2018. "Bayesian Variance Changepoint Detection in Linear Models with Symmetric Heavy-Tailed Errors," Computational Economics, Springer;Society for Computational Economics, vol. 52(2), pages 459-477, August.

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