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Title: Do maternal health problems influence child's worrying status? Evidence from the British Cohort Study
Authors: Dai, X
Karl Härdle, W
Yu, K
Keywords: British Cohort Study data;Bayesian inference;Quantile regression;Asymmetric Laplace error distribution;Markov chain Monte Carlo;Stochastic search variableselection;C11;C38;C63
Issue Date: 2016
Publisher: Taylor & Francis
Citation: Journal of Applied Statistics: pp.1 - 15, (2016)
Abstract: Conventional methods apply symmetric prior distributions such as a normal distribution or a Laplace distribution for regression coefficients, which may be suitable for median regression and exhibit no robustness to outliers. This work develops a quantile regression on linear panel data model without heterogeneity from a Bayesian point of view, i.e. upon a location-scale mixture representation of the asymmetric Laplace error distribution, and provides how the posterior distribution is summarized using Markov chain Monte Carlo methods. Applying this approach to the 1970 British Cohort Study (BCS) data, it finds 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 to the 1970 BCS data, it finds that maternal nervous breakdown, among the 25 maternal health problems, contributes most to influence the child's worrying status.
ISSN: 0266-4763
Appears in Collections:Dept of Mathematics Research Papers

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