Testing multivariate quantile by empirical likelihood
Author
Abstract
Suggested Citation
DOI: 10.1016/j.jmva.2020.104705
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
- Biman Chakraborty, 2001. "On Affine Equivariant Multivariate Quantiles," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 53(2), pages 380-403, June.
- Nordhausen, Klaus & Oja, Hannu, 2011. "Multivariate L1 Statistical Methods: The Package MNM," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 43(i05).
- Srijan Sengupta & Stanislav Volgushev & Xiaofeng Shao, 2016. "A Subsampled Double Bootstrap for Massive Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1222-1232, July.
- Ariel Kleiner & Ameet Talwalkar & Purnamrita Sarkar & Michael I. Jordan, 2014. "A scalable bootstrap for massive data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(4), pages 795-816, September.
- Robert Serfling, 2002. "Quantile functions for multivariate analysis: approaches and applications," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 56(2), pages 214-232, May.
- Christophe Ley & Camille Sabbah & Thomas Verdebout, 2014. "A new concept of quantiles for directional data and the angular Mahalanobis depth," Working Papers ECARES ECARES 2013-23, ULB -- Universite Libre de Bruxelles.
- Shi, Chengchun & Lu, Wenbin & Song, Rui, 2018. "A massive data framework for M-estimators with cubic-rate," LSE Research Online Documents on Economics 102111, London School of Economics and Political Science, LSE Library.
- Chengchun Shi & Wenbin Lu & Rui Song, 2018. "A Massive Data Framework for M-Estimators with Cubic-Rate," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(524), pages 1698-1709, October.
- HaiYing Wang & Rong Zhu & Ping Ma, 2018. "Optimal Subsampling for Large Sample Logistic Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 829-844, April.
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.- Xuejun Ma & Shaochen Wang & Wang Zhou, 2022. "Statistical inference in massive datasets by empirical likelihood," Computational Statistics, Springer, vol. 37(3), pages 1143-1164, July.
- Zhang, Haixiang & Wang, HaiYing, 2021. "Distributed subdata selection for big data via sampling-based approach," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).
- Baolin Chen & Shanshan Song & Yong Zhou, 2024. "Estimation and testing of expectile regression with efficient subsampling for massive data," Statistical Papers, Springer, vol. 65(9), pages 5593-5613, December.
- Lulu Zuo & Haixiang Zhang & HaiYing Wang & Liuquan Sun, 2021. "Optimal subsample selection for massive logistic regression with distributed data," Computational Statistics, Springer, vol. 36(4), pages 2535-2562, December.
- Guangbao Guo & Yue Sun & Xuejun Jiang, 2020. "A partitioned quasi-likelihood for distributed statistical inference," Computational Statistics, Springer, vol. 35(4), pages 1577-1596, December.
- Mohamed CHAOUCH & Ali GANNOUN & Jérôme SARACCO, 2008. "Conditional Spatial Quantile: Characterization and Nonparametric Estimation," Cahiers du GREThA (2007-2019) 2008-10, Groupe de Recherche en Economie Théorique et Appliquée (GREThA).
- Nadja Klein & Thomas Kneib, 2020. "Directional bivariate quantiles: a robust approach based on the cumulative distribution function," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(2), pages 225-260, June.
- Dimitris N Politis, 2024. "Scalable subsampling: computation, aggregation and inference," Biometrika, Biometrika Trust, vol. 111(1), pages 347-354.
- Amalan Mahendran & Helen Thompson & James M. McGree, 2023. "A model robust subsampling approach for Generalised Linear Models in big data settings," Statistical Papers, Springer, vol. 64(4), pages 1137-1157, August.
- Baumeister, Marléne & Ditzhaus, Marc & Pauly, Markus, 2024. "Quantile-based MANOVA: A new tool for inferring multivariate data in factorial designs," Journal of Multivariate Analysis, Elsevier, vol. 199(C).
- Chaouch, Mohamed & Goga, Camelia, 2010. "Design-based estimation for geometric quantiles with application to outlier detection," Computational Statistics & Data Analysis, Elsevier, vol. 54(10), pages 2214-2229, October.
- Tom Boot & Art=uras Juodis, 2023. "Uniform Inference in Linear Error-in-Variables Models: Divide-and-Conquer," Papers 2301.04439, arXiv.org.
- Bingyao Huang & Yanyan Liu & Liuhua Peng, 2023. "Distributed inference for two‐sample U‐statistics in massive data analysis," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(3), pages 1090-1115, September.
- Chen, Canyi & Xu, Wangli & Zhu, Liping, 2022. "Distributed estimation in heterogeneous reduced rank regression: With application to order determination in sufficient dimension reduction," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
- Marc Hallin & Davy Paindaveine & Miroslav Siman, 2008.
"Multivariate quantiles and multiple-output regression quantiles: from L1 optimization to halfspace depth,"
Working Papers ECARES
2008_042, ULB -- Universite Libre de Bruxelles.
- Marc Hallin & Davy Paindaveine & Miroslav Šiman, 2010. "Multivariate quantiles and multiple-output regression quantiles: From L1 optimization to halfspace depth," ULB Institutional Repository 2013/127979, ULB -- Universite Libre de Bruxelles.
- Fengrui Di & Lei Wang, 2022. "Multi-round smoothed composite quantile regression for distributed data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(5), pages 869-893, October.
- Klaus Herrmann & Marius Hofert & Melina Mailhot, 2017. "Multivariate Geometric Expectiles," Papers 1704.01503, arXiv.org, revised Jan 2018.
- Zhao, Yan-Yong & Zhang, Yuchun & Liu, Yuan & Ismail, Noriszura, 2024. "Distributed debiased estimation of high-dimensional partially linear models with jumps," Computational Statistics & Data Analysis, Elsevier, vol. 191(C).
- Le-Yu Chen & Sokbae Lee, 2018. "High Dimensional Classification through $\ell_0$-Penalized Empirical Risk Minimization," Papers 1811.09540, arXiv.org.
- Lee, JooChul & Wang, HaiYing & Schifano, Elizabeth D., 2020. "Online updating method to correct for measurement error in big data streams," Computational Statistics & Data Analysis, Elsevier, vol. 149(C).
More about this item
Keywords
Empirical likelihood; Hypothesis test; Mean-of-quantile; Multivariate quantile;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:182:y:2021:i:c:s0047259x20302864. 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.