A joint quantile regression model for multiple longitudinal outcomes

Hemant Kulkarni, Jayabrata Biswas and Kiranmoy Das ()
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Hemant Kulkarni: Indian Statistical Institute
Jayabrata Biswas: Indian Statistical Institute
Kiranmoy Das: Indian Statistical Institute

AStA Advances in Statistical Analysis, 2019, vol. 103, issue 4, No 1, 453-473

Abstract: 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.

Keywords: Asymmetric Laplace Distribution; EM algorithm; Longitudinal data; MCMC; Quantile regression (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10182-018-00339-9

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