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On sufficient dimension reduction for proportional censorship model with covariates

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  • Wen, Xuerong Meggie

Abstract

The requirement of constant censoring parameter [beta] in Koziol-Green (KG) model is too restrictive. When covariates are present, the conditional KG model (Veraverbekea and Cadarso-Suárez, 2000) which allows [beta] to be dependent on the covariates is more realistic. In this paper, using sufficient dimension reduction methods, we provide a model-free diagnostic tool to test if [beta] is a function of the covariates. Our method also allows us to conduct a model-free selection of the related covariates. A simulation study and a real data analysis are also included to illustrate our approach.

Suggested Citation

  • Wen, Xuerong Meggie, 2010. "On sufficient dimension reduction for proportional censorship model with covariates," Computational Statistics & Data Analysis, Elsevier, vol. 54(8), pages 1975-1982, August.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:8:p:1975-1982
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    References listed on IDEAS

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    1. Yingcun Xia & Howell Tong & W. K. Li & Li‐Xing Zhu, 2002. "An adaptive estimation of dimension reduction space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 363-410, August.
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    Cited by:

    1. Yoo, Jae Keun, 2015. "A theoretical note on optimal sufficient dimension reduction with singularity," Statistics & Probability Letters, Elsevier, vol. 99(C), pages 109-113.

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