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A joint quantile regression model for multiple longitudinal outcomes

Author

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  • Hemant Kulkarni

    (Indian Statistical Institute)

  • Jayabrata Biswas

    (Indian Statistical Institute)

  • Kiranmoy Das

    (Indian Statistical Institute)

Abstract

Complexity of longitudinal data lies in the inherent dependence among measurements from same subject over different time points. For multiple longitudinal responses, the problem is challenging due to inter-trait and intra-trait dependence. While linear mixed models are popularly used for analysing such data, appropriate inference on the shape of the population cannot be drawn for non-normal data sets. We propose a linear mixed model for joint quantile regression of multiple longitudinal responses. We consider an asymmetric Laplace distribution for quantile regression and estimate model parameters by Monte Carlo EM algorithm. Nonparametric bootstrap resampling method is used for estimating confidence intervals of parameter estimates. Through extensive simulation studies, we investigate the operating characteristics of our proposed model and compare the performance to other traditional quantile regression models. We apply proposed model for analysing data from nutrition education programme on hypercholesterolemic children of the USA.

Suggested Citation

  • Hemant Kulkarni & Jayabrata Biswas & Kiranmoy Das, 2019. "A joint quantile regression model for multiple longitudinal outcomes," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(4), pages 453-473, December.
    • Handle: RePEc:spr:alstar:v:103:y:2019:i:4:d:10.1007_s10182-018-00339-9
    DOI: 10.1007/s10182-018-00339-9
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    References listed on IDEAS

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    Cited by:

    1. Luca Merlo & Lea Petrella & Nikos Tzavidis, 2022. "Quantile mixed hidden Markov models for multivariate longitudinal data: An application to children's Strengths and Difficulties Questionnaire scores," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(2), pages 417-448, March.
    2. Marco Alfò & Maria Francesca Marino & Maria Giovanna Ranalli & Nicola Salvati & Nikos Tzavidis, 2021. "M‐quantile regression for multivariate longitudinal data with an application to the Millennium Cohort Study," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 122-146, January.
    3. Łukasz Jarosław Kozar & Robert Matusiak & Marta Paduszyńska & Adam Sulich, 2022. "Green Jobs in the EU Renewable Energy Sector: Quantile Regression Approach," Energies, MDPI, vol. 15(18), pages 1-21, September.
    4. Jayabrata Biswas & Kiranmoy Das, 2021. "A Bayesian quantile regression approach to multivariate semi-continuous longitudinal data," Computational Statistics, Springer, vol. 36(1), pages 241-260, March.

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