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Generalized martingale difference divergence: Detecting conditional mean independence with applications in variable screening

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  • Li, Lu
  • Ke, Chenlu
  • Yin, Xiangrong
  • Yu, Zhou

Abstract

Martingale difference divergence measures the departure of conditional mean independence of two random vectors. Generalized martingale difference divergence and its correlation are developed based on symmetric Lévy measures to detect such an independence. Then the proposed generalized martingale difference correlation is utilized as a marginal utility to do high-dimensional variable screening. Both simulation results and real data illustrations show the promising performance of the developed indexes.

Suggested Citation

  • Li, Lu & Ke, Chenlu & Yin, Xiangrong & Yu, Zhou, 2023. "Generalized martingale difference divergence: Detecting conditional mean independence with applications in variable screening," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    • Handle: RePEc:eee:csdana:v:180:y:2023:i:c:s0167947322001980
    DOI: 10.1016/j.csda.2022.107618
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    References listed on IDEAS

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    6. 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.
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    8. Chung Eun Lee & Xiaofeng Shao, 2020. "Volatility Martingale Difference Divergence Matrix and Its Application to Dimension Reduction for Multivariate Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 80-92, January.
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    Cited by:

    1. Ke, Chenlu & Yang, Wei & Yuan, Qingcong & Li, Lu, 2023. "Partial sufficient variable screening with categorical controls," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).

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