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Advances in seeded dimension reduction: Bootstrap criteria and extensions

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  • Yoo, Jae Keun

Abstract

A seeded dimension reduction approach recently developed provides a paradigm to enable existing dimension reduction methods for the central subspace to be adapted to regressions with n

Suggested Citation

  • Yoo, Jae Keun, 2013. "Advances in seeded dimension reduction: Bootstrap criteria and extensions," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 70-79.
    • Handle: RePEc:eee:csdana:v:60:y:2013:i:c:p:70-79
    DOI: 10.1016/j.csda.2012.10.003
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    References listed on IDEAS

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    1. Lexin Li, 2007. "Sparse sufficient dimension reduction," Biometrika, Biometrika Trust, vol. 94(3), pages 603-613.
    2. Liqiang Ni & R. Dennis Cook & Chih-Ling Tsai, 2005. "A note on shrinkage sliced inverse regression," Biometrika, Biometrika Trust, vol. 92(1), pages 242-247, March.
    3. 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.
    4. R. Dennis Cook & Bing Li & Francesca Chiaromonte, 2007. "Dimension reduction in regression without matrix inversion," Biometrika, Biometrika Trust, vol. 94(3), pages 569-584.
    5. Lexin Li & Xiangrong Yin, 2008. "Sliced Inverse Regression with Regularizations," Biometrics, The International Biometric Society, vol. 64(1), pages 124-131, March.
    6. Francesca Chiaromonte & R. Cook, 2002. "Sufficient Dimension Reduction and Graphics in Regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 54(4), pages 768-795, December.
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