The influence of maternal health problems on child's worrying status is important in practice in terms of the intervention of maternal health problems early for the influence on child's worrying status. Conventional methods apply symmetric prior distributions such as a normal distribution or a Laplace distribituion for regression coefficients, which may be suitable for median regression and exhibit no robustness to outliers. This paper develops a quantile regression on linear panel data model without heterogeneity from a Bayesian point of view, and examines the influence of maternal health problems on child's worrying status. Upon a location-scale mixture representation of the asymmetric Laplace error distribution, this paper provides how the posterior distribution can be sampled and summarized by Markov chain Monte Carlo method. Applying for the 1970 British Cohort Study data, we find and that a different maternal health problem has different influence on child's worrying status at different quantiles. In addition, applying stochastic search variable selection for maternal health problems in the 1970 British Cohort Study data, we find that maternal nervous breakdown, in our work, among the 25 maternal health problems, contributes most to influence the child's worrying status.
British Cohort Study data Bayesian inference Quantile regression Asymmetric Laplace error distribution Markov chain Monte Carlo Variable selection