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Latent variable model for mixed correlated power series and ordinal longitudinal responses with non ignorable missing values

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  • F. Razie
  • E. Bahrami Samani
  • M. Ganjali

Abstract

We propose a joint model based on a latent variable for analyzing mixed power series and ordinal longitudinal data with and without missing values. A bivariate probit regression model is used for the missing mechanisms. Random effects are used to take into account the correlation between longitudinal responses. A full likelihood-based approach is used to yield maximum-likelihood estimates of the model parameters. Our model is applied to a medical data set, obtained from an observational study on women where the correlated responses are the ordinal response of osteoporosis of the spine and the power series response of the number of joint damages. Sensitivity analysis is also performed to study the influence of small perturbations of the parameters of the missing mechanisms and overdispersion of the model on likelihood displacement.

Suggested Citation

  • F. Razie & E. Bahrami Samani & M. Ganjali, 2017. "Latent variable model for mixed correlated power series and ordinal longitudinal responses with non ignorable missing values," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(12), pages 5738-5753, June.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:12:p:5738-5753
    DOI: 10.1080/03610926.2015.1105980
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    Cited by:

    1. Yu-Zhu Tian & Man-Lai Tang & Wai-Sum Chan & Mao-Zai Tian, 2021. "Bayesian bridge-randomized penalized quantile regression for ordinal longitudinal data, with application to firm’s bond ratings," Computational Statistics, Springer, vol. 36(2), pages 1289-1319, June.

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