Distributional data analysis of accelerometer data from the NHANES database using nonparametric survey regression models

Abstract

Abstract The aim of this paper is twofold. First, a new functional representation of accelerometer data of a distributional nature is introduced to build a complete individualized profile of each subject’s physical activity levels. Second, we extend two nonparametric functional regression models, kernel smoothing and kernel ridge regression, to handle survey data and obtain reliable conclusions about the influence of physical activity. The advantages of the proposed distributional representation are demonstrated through various analyses performed on the NHANES cohort, which possesses a complex sampling design.