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Partial moment-based sufficient dimension reduction

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  • Yoo, Jae Keun

Abstract

In this paper, a partial moment-based sufficient dimension reduction methodology is developed. This extends and generalizes some existing methods and enables us to test predictor contributions. All tests in the proposed method are done with chi-squared distributions.

Suggested Citation

  • Yoo, Jae Keun, 2009. "Partial moment-based sufficient dimension reduction," Statistics & Probability Letters, Elsevier, vol. 79(4), pages 450-456, February.
    • Handle: RePEc:eee:stapro:v:79:y:2009:i:4:p:450-456
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    References listed on IDEAS

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    1. Xiangrong Yin & R. Dennis Cook, 2002. "Dimension reduction for the conditional kth moment in regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 159-175, May.
    2. Yoo, Jae Keun, 2008. "A novel moment-based sufficient dimension reduction approach in multivariate regression," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3843-3851, March.
    3. Jae Keun Yoo & R. Dennis Cook, 2007. "Optimal sufficient dimension reduction for the conditional mean in multivariate regression," Biometrika, Biometrika Trust, vol. 94(1), pages 231-242.
    4. Cook, R. Dennis & Ni, Liqiang, 2005. "Sufficient Dimension Reduction via Inverse Regression: A Minimum Discrepancy Approach," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 410-428, June.
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    Cited by:

    1. Hilafu, Haileab & Yin, Xiangrong, 2013. "Sufficient dimension reduction in multivariate regressions with categorical predictors," Computational Statistics & Data Analysis, Elsevier, vol. 63(C), pages 139-147.
    2. Park, Yujin & Kim, Kyongwon & Yoo, Jae Keun, 2022. "On cross-distance selection algorithm for hybrid sufficient dimension reduction," Computational Statistics & Data Analysis, Elsevier, vol. 176(C).

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