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Tests for Coefficients in High-dimensional Additive Hazard Models

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  • Ping-Shou Zhong
  • Tao Hu
  • Jun Li

Abstract

type="main" xml:id="sjos12127-abs-0001"> We consider hypothesis testing problems for low-dimensional coefficients in a high dimensional additive hazard model. A variance reduced partial profiling estimator (VRPPE) is proposed and its asymptotic normality is established, which enables us to test the significance of each single coefficient when the data dimension is much larger than the sample size. Based on the p-values obtained from the proposed test statistics, we then apply a multiple testing procedure to identify significant coefficients and show that the false discovery rate can be controlled at the desired level. The proposed method is also extended to testing a low-dimensional sub-vector of coefficients. The finite sample performance of the proposed testing procedure is evaluated by simulation studies. We also apply it to two real data sets, with one focusing on testing low-dimensional coefficients and the other focusing on identifying significant coefficients through the proposed multiple testing procedure.

Suggested Citation

  • Ping-Shou Zhong & Tao Hu & Jun Li, 2015. "Tests for Coefficients in High-dimensional Additive Hazard Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(3), pages 649-664, September.
    • Handle: RePEc:bla:scjsta:v:42:y:2015:i:3:p:649-664
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    File URL: http://hdl.handle.net/10.1111/sjos.12127
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    References listed on IDEAS

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    Cited by:

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