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.
翻译:我们提议在非线性函数在天际上回归模型中采用内核机的假设测试程序。我们的研究受新生的神经基因研究(NEST)的驱动,该研究的问题是,在9个不同甲基区域中,任何1个预先指定的有毒金属或甲基化是否与儿童生长有关。我们把儿童生长轨迹作为功能反应,用非线性函数共同模拟有毒金属测量。我们用内核机法模拟未知的功能,并将无效的假设转换为适当的差异组成部分测试。我们用模拟研究来展示我们提出的方法,并应用它来分析 NEST数据。