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Nonparametric conditional mean testing via an extreme‐type statistic in high dimension

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  • Yiming Liu
  • Guangming Pan
  • Guangren Yang
  • Wang Zhou

Abstract

We propose a new test to investigate the conditional mean dependence between a response variable and the corresponding covariates in the high‐dimensional regimes. The test statistic is an extreme‐type one built on the nonparametric method. The limiting null distribution of the proposed extreme type statistic under a mild mixing condition is established. Moreover, to make the test more powerful in general structures we propose a more general test statistic and develop its asymptotic properties. The power analysis of both methods is also considered. In real data analysis, we also propose a new way to conduct the feature screening based on our results. To evaluate the performance of our estimators and other methods, extensive simulations are conducted.

Suggested Citation

  • Yiming Liu & Guangming Pan & Guangren Yang & Wang Zhou, 2024. "Nonparametric conditional mean testing via an extreme‐type statistic in high dimension," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 51(2), pages 801-831, June.
    • Handle: RePEc:bla:scjsta:v:51:y:2024:i:2:p:801-831
    DOI: 10.1111/sjos.12697
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    References listed on IDEAS

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    5. Xiaofeng Shao & Jingsi Zhang, 2014. "Martingale Difference Correlation and Its Use in High-Dimensional Variable Screening," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1302-1318, September.
    6. Tony Cai & Weidong Liu & Yin Xia, 2013. "Two-Sample Covariance Matrix Testing and Support Recovery in High-Dimensional and Sparse Settings," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 265-277, March.
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