title: Modeling and prediction of children's growth data via functional principal component analysis.
abstract: We use the functional principal component analysis (FPCA) to model and predict the weight growth in children. In particular, we examine how the approach can help discern growth patterns of underweight children relative to their normal counterparts, and whether a commonly used transformation to normality plays any constructive roles in a predictive model based on the FPCA. Our work supplements the conditional growth charts developed by Wei and He (2006) by constructing a predictive growth model based on a small number of principal components scores on individual's past.
authors.affiliation: Key Laboratory for Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun 130024, Jilin, P.R. China.
authors.forename: Hu
authors.initials: H
authors.lastname: Yu
authors.forename: He
authors.initials: H
authors.lastname: Xuming
authors.forename: Tao
authors.initials: T
authors.lastname: Jian
authors.forename: Shi
authors.initials: S
authors.lastname: Ning-Zhong