Comments on: High-dimensional simultaneous inference with the bootstrap
Author
Abstract
Suggested Citation
DOI: 10.1007/s11749-017-0558-y
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
- Ruben Dezeure & Peter Bühlmann & Cun-Hui Zhang, 2017. "High-dimensional simultaneous inference with the bootstrap," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(4), pages 685-719, December.
- Ruben Dezeure & Peter Bühlmann & Cun-Hui Zhang, 2017. "Rejoinder on: High-dimensional simultaneous inference with the bootstrap," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(4), pages 751-758, December.
- 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.
- Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors," CeMMAP working papers 76/13, Institute for Fiscal Studies.
- Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors," CeMMAP working papers CWP76/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Yu I. Ingster & Alexandre B. Tsybakov & N. Verzelzn, 2010. "Detection Boundary in Sparse Regression," Working Papers 2010-28, Center for Research in Economics and Statistics.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Ruben Dezeure & Peter Bühlmann & Cun-Hui Zhang, 2017. "Rejoinder on: High-dimensional simultaneous inference with the bootstrap," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(4), pages 751-758, December.
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.- Victor Chernozhukov & Wolfgang K. Hardle & Chen Huang & Weining Wang, 2018.
"LASSO-Driven Inference in Time and Space,"
Papers
1806.05081, arXiv.org, revised May 2020.
- Chernozhukov, V. & Härdle, W.K. & Huang, C. & Wang, W., 2018. "LASSO-Driven Inference in Time and Space," Working Papers 18/04, Department of Economics, City University London.
- Victor Chernozhukov & Wolfgang Härdle & Chen Huang & Weining Wang, 2019. "LASSO-Driven Inference in Time and Space," CeMMAP working papers CWP20/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Chernozhukov, Victor & Härdle, Wolfgang Karl & Huang, Chen & Wang, Weining, 2018. "LASSO-Driven Inference in Time and Space," IRTG 1792 Discussion Papers 2018-021, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
- Victor Chernozhukov & Wolfgang Härdle & Chen Huang & Weining Wang, 2018. "LASSO-driven inference in time and space," CeMMAP working papers CWP36/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Xingcai Zhou & Zhaoyang Jing & Chao Huang, 2024. "Distributed Bootstrap Simultaneous Inference for High-Dimensional Quantile Regression," Mathematics, MDPI, vol. 12(5), pages 1-54, February.
- Tanin Sirimongkolkasem & Reza Drikvandi, 2019. "On Regularisation Methods for Analysis of High Dimensional Data," Annals of Data Science, Springer, vol. 6(4), pages 737-763, December.
- He, Yi & Jaidee, Sombut & Gao, Jiti, 2023. "Most powerful test against a sequence of high dimensional local alternatives," Journal of Econometrics, Elsevier, vol. 234(1), pages 151-177.
- Martin C. Arnold & Thilo Reinschlussel, 2024. "Bootstrap Adaptive Lasso Solution Path Unit Root Tests," Papers 2409.07859, arXiv.org.
- Jeng, X. Jessie & Chen, Xiongzhi, 2019. "Predictor ranking and false discovery proportion control in high-dimensional regression," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 163-175.
- Byol Kim & Song Liu & Mladen Kolar, 2021. "Two‐sample inference for high‐dimensional Markov networks," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 939-962, November.
- Adamek, Robert & Smeekes, Stephan & Wilms, Ines, 2023.
"Lasso inference for high-dimensional time series,"
Journal of Econometrics, Elsevier, vol. 235(2), pages 1114-1143.
- Robert Adamek & Stephan Smeekes & Ines Wilms, 2020. "Lasso Inference for High-Dimensional Time Series," Papers 2007.10952, arXiv.org, revised Sep 2022.
- Claude Renaux & Laura Buzdugan & Markus Kalisch & Peter Bühlmann, 2020. "Hierarchical inference for genome-wide association studies: a view on methodology with software," Computational Statistics, Springer, vol. 35(1), pages 1-40, March.
- Rui Wang & Xingzhong Xu, 2021. "A Bayesian-motivated test for high-dimensional linear regression models with fixed design matrix," Statistical Papers, Springer, vol. 62(4), pages 1821-1852, August.
- Hanzhong Liu & Bin Yu, 2017. "Comments on: High-dimensional simultaneous inference with the bootstrap," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(4), pages 740-750, December.
- Victor Chernozhukov & Denis Chetverikov & Kengo Kato & Yuta Koike, 2022. "High-dimensional Data Bootstrap," Papers 2205.09691, arXiv.org.
- Belloni, Alexandre & Hansen, Christian & Newey, Whitney, 2022. "High-dimensional linear models with many endogenous variables," Journal of Econometrics, Elsevier, vol. 228(1), pages 4-26.
- Brice Ozenne & Esben Budtz-Jørgensen & Sebastian Elgaard Ebert, 2023. "Controlling the familywise error rate when performing multiple comparisons in a linear latent variable model," Computational Statistics, Springer, vol. 38(1), pages 1-23, March.
- 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.
- 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.
- 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.
- 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.
- Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013.
"Uniform post selection inference for LAD regression and other z-estimation problems,"
CeMMAP working papers
CWP74/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Uniform post selection inference for LAD regression and other z-estimation problems," CeMMAP working papers 74/13, Institute for Fiscal Studies.
- Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2014. "Uniform post selection inference for LAD regression and other Z-estimation problems," CeMMAP working papers 51/14, Institute for Fiscal Studies.
- Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Uniform Post Selection Inference for LAD Regression and Other Z-estimation problems," Papers 1304.0282, arXiv.org, revised Oct 2020.
- Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2014. "Uniform post selection inference for LAD regression and other Z-estimation problems," CeMMAP working papers CWP51/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Ian W. McKeague & Min Qian, 2015. "An Adaptive Resampling Test for Detecting the Presence of Significant Predictors," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1422-1433, December.
- Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey, 2016.
"Double machine learning for treatment and causal parameters,"
CeMMAP working papers
49/16, Institute for Fiscal Studies.
- Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey, 2016. "Double machine learning for treatment and causal parameters," CeMMAP working papers CWP49/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Wu Wang & Xuming He & Zhongyi Zhu, 2020. "Statistical inference for multiple change‐point models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(4), pages 1149-1170, December.
- Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler & Sven Klaassen, 2021. "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R," Papers 2103.09603, arXiv.org, revised Jun 2024.
Corrections
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:spr:testjl:v:26:y:2017:i:4:d:10.1007_s11749-017-0558-y. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.