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Linear and conic programming estimators in high dimensional errors-in-variables models

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  • Alexandre Belloni
  • Mathieu Rosenbaum
  • Alexandre B. Tsybakov

Abstract

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  • Alexandre Belloni & Mathieu Rosenbaum & Alexandre B. Tsybakov, 2017. "Linear and conic programming estimators in high dimensional errors-in-variables models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 939-956, June.
    • Handle: RePEc:bla:jorssb:v:79:y:2017:i:3:p:939-956
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    File URL: http://hdl.handle.net/10.1111/rssb.12196
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    References listed on IDEAS

    as
    1. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2012. "Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors," Papers 1212.6906, arXiv.org, revised Jan 2018.
    2. Eric Gautier & Alexandre Tsybakov, 2011. "High-Dimensional Instrumental Variables Regression and Confidence Sets," Working Papers 2011-13, Center for Research in Economics and Statistics.
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    Citations

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    Cited by:

    1. Baris Ata & Alexandre Belloni & Ozan Candogan, 2018. "Latent Agents in Networks: Estimation and Targeting," Papers 1808.04878, arXiv.org, revised Jan 2022.
    2. Ziwei Zhu & Tengyao Wang & Richard J. Samworth, 2022. "High‐dimensional principal component analysis with heterogeneous missingness," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 2000-2031, November.
    3. Yumou Qiu & Jing Tao & Xiao‐Hua Zhou, 2021. "Inference of heterogeneous treatment effects using observational data with high‐dimensional covariates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 1016-1043, November.
    4. Jingxuan Luo & Lili Yue & Gaorong Li, 2023. "Overview of High-Dimensional Measurement Error Regression Models," Mathematics, MDPI, vol. 11(14), pages 1-22, July.
    5. Zhu, Ziwei & Wang, Tengyao & Samworth, Richard J., 2022. "High-dimensional principal component analysis with heterogeneous missingness," LSE Research Online Documents on Economics 117647, London School of Economics and Political Science, LSE Library.
    6. Wu, Jie & Zheng, Zemin & Li, Yang & Zhang, Yi, 2020. "Scalable interpretable learning for multi-response error-in-variables regression," Journal of Multivariate Analysis, Elsevier, vol. 179(C).
    7. Wei Liu & Wenbo Wu & Baoying Yang, 2026. "Feature Screening for High-Dimensional Data with Measurement Errors using Adjusted Martingale Difference Correlation," Statistical Papers, Springer, vol. 67(2), pages 1-49, April.
    8. Alexandre Belloni & Victor Chernozhukov & Abhishek Kaul & Mathieu Rosenbaum & Alexandre B. Tsybakov, 2017. "Pivotal Estimation Via Self-Normalization for High-Dimensional Linear Models with Errors in Variables," Working Papers 2017-26, Center for Research in Economics and Statistics.
    9. Li, Mengyan & Li, Runze & Ma, Yanyuan, 2021. "Inference in high dimensional linear measurement error models," Journal of Multivariate Analysis, Elsevier, vol. 184(C).
    10. Galea, Manuel & de Castro, Mário, 2017. "Robust inference in a linear functional model with replications using the t distribution," Journal of Multivariate Analysis, Elsevier, vol. 160(C), pages 134-145.
    11. Xin Li & Dongya Wu, 2024. "Low-rank matrix estimation via nonconvex optimization methods in multi-response errors-in-variables regression," Journal of Global Optimization, Springer, vol. 88(1), pages 79-114, January.
    12. Li, Xin & Wu, Dongya, 2025. "Low-rank matrix estimation via nonconvex spectral regularized methods in errors-in-variables matrix regression," European Journal of Operational Research, Elsevier, vol. 323(2), pages 626-641.
    13. Linh H. Nghiem & Francis K.C. Hui & Samuel Müller & A.H. Welsh, 2023. "Screening methods for linear errors‐in‐variables models in high dimensions," Biometrics, The International Biometric Society, vol. 79(2), pages 926-939, June.

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