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Partially varying coefficient single-index additive hazard models

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  • Xuan Wang
  • Qihua Wang
  • Xiao-Hua Zhou

Abstract

The partially linear additive hazards model has been proposed to study the interaction between some covariates and an exposure variable. In this paper, we extend it to the partially varying coefficient single-index additive hazard model where the high dimension covariates are collapsed to a single index, due to practical needs. Two sets of estimating equations were proposed to estimate the varying coefficient functions in the linear components: the link function for the single index and the single-index parameter vector separately. It was shown that the proposed local and global estimators are asymptotically normal. Simulation studies were conducted to examine the finite-sample performance of our method to compare the relative performance of our method with existing ones. A real data analysis was used to illustrate the proposed methods. Copyright The Institute of Statistical Mathematics, Tokyo 2015

Suggested Citation

  • Xuan Wang & Qihua Wang & Xiao-Hua Zhou, 2015. "Partially varying coefficient single-index additive hazard models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(5), pages 817-841, October.
    • Handle: RePEc:spr:aistmt:v:67:y:2015:i:5:p:817-841
    DOI: 10.1007/s10463-014-0484-7
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    References listed on IDEAS

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    1. Yu Y. & Ruppert D., 2002. "Penalized Spline Estimation for Partially Linear Single-Index Models," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1042-1054, December.
    2. Lu, Wenbin & Zhang, Hao Helen, 2010. "On Estimation of Partially Linear Transformation Models," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 683-691.
    3. Kani Chen & Xingwei Tong, 2010. "Varying coefficient transformation models with censored data," Biometrika, Biometrika Trust, vol. 97(4), pages 969-976.
    4. Lu Tian & David Zucker & L.J. Wei, 2005. "On the Cox Model With Time-Varying Regression Coefficients," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 172-183, March.
    5. Zongwu Cai & Yanqing Sun, 2003. "Local Linear Estimation for Time‐Dependent Coefficients in Cox's Regression Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 30(1), pages 93-111, March.
    6. Xia, Yingcun & Härdle, Wolfgang, 2006. "Semi-parametric estimation of partially linear single-index models," Journal of Multivariate Analysis, Elsevier, vol. 97(5), pages 1162-1184, May.
    7. Kani Chen, 2002. "Semiparametric analysis of transformation models with censored data," Biometrika, Biometrika Trust, vol. 89(3), pages 659-668, August.
    8. Yin, Guosheng & Li, Hui & Zeng, Donglin, 2008. "Partially Linear Additive Hazards Regression With Varying Coefficients," Journal of the American Statistical Association, American Statistical Association, vol. 103(483), pages 1200-1213.
    9. Peng, Limin & Huang, Yijian, 2008. "Survival Analysis With Quantile Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 637-649, June.
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