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Ultrahigh-dimensional sufficient dimension reduction with measurement error in covariates

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  • Chen, Li-Pang

Abstract

Analysis of sufficient dimension reduction (SDR) is an important topic and has attracts our attention in decades. Several methods have been proposed based on simple settings. In applications, however, the ultrahigh-dimensional setting with p≫n and covariate measurement error usually appear in the dataset, and it is not trivial to adopt the conventional methods to handle this problem. In this paper, we consider the SDR with both the ultrahigh-dimensional setting and covariate measurement error incorporated simultaneously. The theoretical results of the proposed method are established.

Suggested Citation

  • Chen, Li-Pang, 2021. "Ultrahigh-dimensional sufficient dimension reduction with measurement error in covariates," Statistics & Probability Letters, Elsevier, vol. 168(C).
    • Handle: RePEc:eee:stapro:v:168:y:2021:i:c:s0167715220302340
    DOI: 10.1016/j.spl.2020.108931
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    References listed on IDEAS

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    4. Zhu, Li-Ping & Zhu, Li-Xing & Feng, Zheng-Hui, 2010. "Dimension Reduction in Regressions Through Cumulative Slicing Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1455-1466.
    5. Li‐Pang Chen, 2019. "Statistical analysis with measurement error or misclassification: Strategy, method and application. Grace Y. Yi. New York: Springer‐Verlag," Biometrics, The International Biometric Society, vol. 75(3), pages 1045-1046, September.
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