1. Test and Measure for Partial Mean Dependence Based on Machine Learning Methods.
- Author
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Cai, Leheng, Guo, Xu, and Zhong, Wei
- Abstract
AbstractIt is of importance to investigate the significance of a subset of covariates
W for the responseY given covariatesZ in regression modeling. To this end, we propose a significance test for the partial mean independence problem based on machine learning methods and data splitting. The test statistic converges to the standard chi-squared distribution under the null hypothesis while it converges to a normal distribution under the fixed alternative hypothesis. Power enhancement and algorithm stability are also discussed. If the null hypothesis is rejected, we propose a partial Generalized Measure of Correlation (pGMC) to measure the partial mean dependence ofY givenW after controlling for the nonlinear effect ofZ . We present the appealing theoretical properties of the pGMC and establish the asymptotic normality of its estimator with the optimal root-N convergence rate. Furthermore, the valid confidence interval for the pGMC is also derived. As an important special case when there are no conditional covariatesZ , we introduce a new test of overall significance of covariates for the response in a model-free setting. Numerical studies and real data analysis are also conducted to compare with existing approaches and to demonstrate the validity and flexibility of our proposed procedures. [ABSTRACT FROM AUTHOR]- Published
- 2024
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