Mathematics – Statistics Theory
Scientific paper
2007-02-22
Annals of Statistics 2006, Vol. 34, No. 5, 2069-2097
Mathematics
Statistics Theory
This paper discussed in: [math/0702636], [math/0702640], [math/0702641], [math/0702642]. Rejoinder in [math.ST/0702643]. Publi
Scientific paper
10.1214/009053606000000623
Growth charts are often more informative when they are customized per subject, taking into account prior measurements and possibly other covariates of the subject. We study a global semiparametric quantile regression model that has the ability to estimate conditional quantiles without the usual distributional assumptions. The model can be estimated from longitudinal reference data with irregular measurement times and with some level of robustness against outliers, and it is also flexible for including covariate information. We propose a rank score test for large sample inference on covariates, and develop a new model assessment tool for longitudinal growth data. Our research indicates that the global model has the potential to be a very useful tool in conditional growth chart analysis.
He Xuming
Wei Ying
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