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A joint quantile regression model for multiple longitudinal outcomes

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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.

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Acknowledgements

The authors sincerely thank Prof. Vern Chinchilli, the Pennsylvania State University, for sharing data from one-year nutrition education.

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Authors and Affiliations

  1. Human Genetics Unit, Indian Statistical Institute, Kolkata, India

    Hemant Kulkarni

  2. Interdisciplinary Statistical Research Unit, Indian Statistical Institute, Kolkata, India

    Jayabrata Biswas & Kiranmoy Das

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  1. Hemant Kulkarni
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  2. Jayabrata Biswas
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  3. Kiranmoy Das
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Correspondence to Kiranmoy Das.

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Kulkarni, H., Biswas, J. & Das, K. A joint quantile regression model for multiple longitudinal outcomes. AStA Adv Stat Anal 103, 453–473 (2019). https://doi.org/10.1007/s10182-018-00339-9

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