Rate optimal estimation and confidence intervals for high-dimensional regression with missing covariates
Author
Abstract
Suggested Citation
DOI: 10.1016/j.jmva.2019.06.004
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
- Cai, Tony & Liu, Weidong & Luo, Xi, 2011. "A Constrained â„“1 Minimization Approach to Sparse Precision Matrix Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 594-607.
- Cun-Hui Zhang & Stephanie S. Zhang, 2014. "Confidence intervals for low dimensional parameters in high dimensional linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 217-242, January.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Yining Wang & Xi Chen & Xiangyu Chang & Dongdong Ge, 2021. "Uncertainty Quantification for Demand Prediction in Contextual Dynamic Pricing," Production and Operations Management, Production and Operations Management Society, vol. 30(6), pages 1703-1717, June.
- Fan, Jinlin & Zhang, Yaowu & Zhu, Liping, 2022. "Independence tests in the presence of measurement errors: An invariance law," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
- Adel Javanmard & Jason D. Lee, 2020. "A flexible framework for hypothesis testing in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 685-718, July.
- Li, Mengyan & Li, Runze & Ma, Yanyuan, 2021. "Inference in high dimensional linear measurement error models," Journal of Multivariate Analysis, Elsevier, vol. 184(C).
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Donggyu Kim, 2024. "High-Dimensional Time-Varying Coefficient Estimation," Working Papers 202416, University of California at Riverside, Department of Economics.
- Donggyu Kim & Minseok Shin, 2024. "Robust High-Dimensional Time-Varying Coefficient Estimation," Working Papers 202417, University of California at Riverside, Department of Economics.
- Gold, David & Lederer, Johannes & Tao, Jing, 2020. "Inference for high-dimensional instrumental variables regression," Journal of Econometrics, Elsevier, vol. 217(1), pages 79-111.
- Li, Xiang & Li, Yu-Ning & Zhang, Li-Xin & Zhao, Jun, 2024. "Inference for high-dimensional linear expectile regression with de-biasing method," Computational Statistics & Data Analysis, Elsevier, vol. 198(C).
- Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018.
"High-dimensional econometrics and regularized GMM,"
CeMMAP working papers
CWP35/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
- Chenchuan (Mark) Li & Ulrich K. Müller, 2021. "Linear regression with many controls of limited explanatory power," Quantitative Economics, Econometric Society, vol. 12(2), pages 405-442, May.
- Jianqing Fan & Xu Han, 2017. "Estimation of the false discovery proportion with unknown dependence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1143-1164, September.
- Guo, Xu & Li, Runze & Liu, Jingyuan & Zeng, Mudong, 2023. "Statistical inference for linear mediation models with high-dimensional mediators and application to studying stock reaction to COVID-19 pandemic," Journal of Econometrics, Elsevier, vol. 235(1), pages 166-179.
- Toshio Honda, 2021. "The de-biased group Lasso estimation for varying coefficient models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(1), pages 3-29, February.
- Hansen, Christian & Liao, Yuan, 2019.
"The Factor-Lasso And K-Step Bootstrap Approach For Inference In High-Dimensional Economic Applications,"
Econometric Theory, Cambridge University Press, vol. 35(3), pages 465-509, June.
- Hansen, Christian & Liao, Yuan, 2016. "The Factor-Lasso and K-Step Bootstrap Approach for Inference in High-Dimensional Economic Applications," MPRA Paper 75313, University Library of Munich, Germany.
- Christian Hansen & Yuan Liao, 2016. "The Factor-Lasso and K-Step Bootstrap Approach for Inference in High-Dimensional Economic Applications," Departmental Working Papers 201610, Rutgers University, Department of Economics.
- Christian Hansen & Yuan Liao, 2016. "The Factor-Lasso and K-Step Bootstrap Approach for Inference in High-Dimensional Economic Applications," Papers 1611.09420, arXiv.org, revised Dec 2016.
- Wang, Tao & Xu, Pei-Rong & Zhu, Li-Xing, 2012. "Non-convex penalized estimation in high-dimensional models with single-index structure," Journal of Multivariate Analysis, Elsevier, vol. 109(C), pages 221-235.
- Jingxuan Luo & Lili Yue & Gaorong Li, 2023. "Overview of High-Dimensional Measurement Error Regression Models," Mathematics, MDPI, vol. 11(14), pages 1-22, July.
- Li, Degui, 2024. "Estimation of Large Dynamic Covariance Matrices: A Selective Review," Econometrics and Statistics, Elsevier, vol. 29(C), pages 16-30.
- Panxu Yuan & Xiao Guo, 2022. "High-dimensional inference for linear model with correlated errors," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(1), pages 21-52, January.
- Agboola, Oluwagbenga David & Yu, Han, 2023. "Neighborhood-based cross fitting approach to treatment effects with high-dimensional data," Computational Statistics & Data Analysis, Elsevier, vol. 186(C).
- Fan, Jianqing & Feng, Yang & Xia, Lucy, 2020. "A projection-based conditional dependence measure with applications to high-dimensional undirected graphical models," Journal of Econometrics, Elsevier, vol. 218(1), pages 119-139.
- Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015.
"Valid Post-Selection and Post-Regularization Inference: An Elementary, General Approach,"
Annual Review of Economics, Annual Reviews, vol. 7(1), pages 649-688, August.
- Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Valid Post-Selection and Post-Regularization Inference: An Elementary, General Approach," Papers 1501.03430, arXiv.org, revised Aug 2015.
- Victor Chernozhukov & Christian Hansen & Martin Spindler, 2016. "Valid post-selection and post-regularization inference: An elementary, general approach," CeMMAP working papers 36/16, Institute for Fiscal Studies.
- Victor Chernozhukov & Christian Hansen & Martin Spindler, 2016. "Valid post-selection and post-regularization inference: An elementary, general approach," CeMMAP working papers CWP36/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Shu Lu & Yufeng Liu & Liang Yin & Kai Zhang, 2017. "Confidence intervals and regions for the lasso by using stochastic variational inequality techniques in optimization," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 589-611, March.
- Guo, Xu & Li, Runze & Liu, Jingyuan & Zeng, Mudong, 2024. "Reprint: Statistical inference for linear mediation models with high-dimensional mediators and application to studying stock reaction to COVID-19 pandemic," Journal of Econometrics, Elsevier, vol. 239(2).
- Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015.
"Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments,"
American Economic Review, American Economic Association, vol. 105(5), pages 486-490, May.
- Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Post-selection and post-regularization inference in linear models with many controls and instruments," CeMMAP working papers 02/15, Institute for Fiscal Studies.
- Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments," Papers 1501.03185, arXiv.org.
- Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Post-selection and post-regularization inference in linear models with many controls and instruments," CeMMAP working papers CWP02/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
More about this item
Keywords
Dantzig selector; De-biasing; High-dimensional regression; Missing data; Confidence intervals; Minimax rates;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jmvana:v:174:y:2019:i:c:s0047259x18304238. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.