Hypothesis Testing in Nonlinear Function on Scalar Regression with Application to Child Growth Study

2019/07/24 by Biswas, Mityl, Maity, Arnab
#Applications (stat.AP) #FOS: Computer and information sciences #Methodology (stat.ME)

paper · doi:10.48550/arxiv.1907.10207

Abstract

We propose a kernel machine based hypothesis testing procedure in nonlinear function-on-scalar regression model. Our research is motivated by the Newborn Epigenetic Study (NEST) where the question of interest is whether a pre-specified group of toxic metals or methylation at any of 9 differentially methylated regions (DMRs) is associated with child growth. We take the child growth trajectory as the functional response, and model the toxic metal measurements jointly using a nonlinear function. We use a kernel machine approach to model the unknown function and transform the hypothesis of no effect to an appropriate variance component test. We demonstrate our proposed methodology using a simulation study and by applying it to analyze the NEST data.

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