Regression based principal component analysis for sparse functional data with applications to screening growth paths

Abstract

Growth charts are widely used in pediatric care for assessing childhood body size measurements (e.g., height or weight). The existing growth charts screen one body size at a single given age. However, when a child has multiple measures over time and exhibits a growth path, how to assess those measures jointly in a rigorous and quantitative way remains largely undeveloped in the literature. In this paper, we develop a new method to construct growth charts for growth paths. A new estimation algorithm using alternating regressions is developed to obtain principal component representations of growth paths (sparse functional data). The new algorithm does not rely on strong distribution assumptions and is computationally robust and easily incorporates subject level covariates, such as parental information. Simulation studies are conducted to investigate the performance of our proposed method, including comparisons to existing methods. When the proposed method is applied to monitor the puberty growth among a group of Finnish teens, it yields interesting insights.