{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T15:29:35Z","timestamp":1768922975221,"version":"3.49.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,14]],"date-time":"2024-12-14T00:00:00Z","timestamp":1734134400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,14]],"date-time":"2024-12-14T00:00:00Z","timestamp":1734134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["11801202"],"award-info":[{"award-number":["11801202"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2021CDJQY-047"],"award-info":[{"award-number":["2021CDJQY-047"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Stat Comput"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s11222-024-10548-9","type":"journal-article","created":{"date-parts":[[2024,12,14]],"date-time":"2024-12-14T10:09:02Z","timestamp":1734170942000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Random perturbation subsampling for rank regression with massive data"],"prefix":"10.1007","volume":"35","author":[{"given":"Sijin","family":"He","sequence":"first","affiliation":[]},{"given":"Xiaochao","family":"Xia","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,14]]},"reference":[{"key":"10548_CR1","doi-asserted-by":"publisher","first-page":"101512","DOI":"10.1016\/j.jco.2020.101512","volume":"62","author":"M Ai","year":"2021","unstructured":"Ai, M., Wang, F., Yu, J., Zhang, H.: Optimal subsampling for large-scale quantile regression. J. Complex. 62, 101512 (2021). https:\/\/doi.org\/10.1016\/j.jco.2020.101512","journal-title":"J. Complex."},{"issue":"2","key":"10548_CR2","doi-asserted-by":"publisher","first-page":"749","DOI":"10.5705\/ss.202018.0439","volume":"31","author":"M Ai","year":"2021","unstructured":"Ai, M., Yu, J., Zhang, H., Wang, H.: Optimal subsampling algorithms for big data regressions. Stat. Sin. 31(2), 749\u2013772 (2021). https:\/\/doi.org\/10.5705\/ss.202018.0439","journal-title":"Stat. Sin."},{"key":"10548_CR3","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-2248-8","volume-title":"U-Statistics, Mm-Estimators and Resampling","author":"A Bose","year":"2018","unstructured":"Bose, A., Chatterjee, S.: U-Statistics, Mm-Estimators and Resampling. Springer, Singapore (2018)"},{"issue":"3","key":"10548_CR4","doi-asserted-by":"publisher","first-page":"1352","DOI":"10.1214\/17-AOS1587","volume":"46","author":"H Battey","year":"2018","unstructured":"Battey, H., Fan, J., Liu, H., Lu, J., Zhu, Z.: Distributed testing and estimation under sparse high dimensional models. Ann. Stat. 46(3), 1352\u20131382 (2018). https:\/\/doi.org\/10.1214\/17-AOS1587","journal-title":"Ann. Stat."},{"issue":"4","key":"10548_CR5","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1007\/s00184-023-00916-2","volume":"87","author":"Y Chao","year":"2024","unstructured":"Chao, Y., Huang, L., Ma, X., Sun, J.: Optimal subsampling for modal regression in massive data. Metrika 87(4), 379\u2013409 (2024). https:\/\/doi.org\/10.1007\/s00184-023-00916-2","journal-title":"Metrika"},{"issue":"1","key":"10548_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-1-4612-4380-9_41","volume":"7","author":"B Efron","year":"1979","unstructured":"Efron, B.: Bootstrap methods: Another look at the jackknife. Ann. Stat. 7(1), 1\u201326 (1979). https:\/\/doi.org\/10.1007\/978-1-4612-4380-9_41","journal-title":"Ann. Stat."},{"key":"10548_CR7","volume-title":"Econometrics","author":"B Hansen","year":"2022","unstructured":"Hansen, B.: Econometrics. Princeton University Press, Princeton (2022)"},{"key":"10548_CR8","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.jspi.2023.02.004","volume":"226","author":"B Huang","year":"2023","unstructured":"Huang, B., Liu, Y., Peng, L.: Weighted bootstrap for two-sample u-statistics. J. Stat. Plan. Inference 226, 86\u201399 (2023). https:\/\/doi.org\/10.1016\/j.jspi.2023.02.004","journal-title":"J. Stat. Plan. Inference"},{"issue":"3","key":"10548_CR9","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1214\/aoms\/1177730196","volume":"19","author":"W Hoeffding","year":"1948","unstructured":"Hoeffding, W.: A class of statistics with asymptotically normal distribution. Ann. Math. Stat. 19(3), 293\u2013325 (1948). https:\/\/doi.org\/10.1214\/aoms\/1177730196","journal-title":"Ann. Math. Stat."},{"issue":"5","key":"10548_CR10","doi-asserted-by":"publisher","first-page":"1449","DOI":"10.1214\/aoms\/1177692377","volume":"43","author":"LA Jaeckel","year":"1972","unstructured":"Jaeckel, L.A.: Estimating regression coefficients by minimizing the dispersion of the residuals. Ann. Math. Stat. 43(5), 1449\u20131458 (1972). https:\/\/doi.org\/10.1214\/aoms\/1177692377","journal-title":"Ann. Math. Stat."},{"issue":"526","key":"10548_CR11","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1080\/01621459.2018.1429274","volume":"114","author":"MI Jordan","year":"2019","unstructured":"Jordan, M.I., Lee, J.D., Yang, Y.: Communication-efficient distributed statistical inference. J. Am. Stat. Assoc. 114(526), 668\u2013681 (2019). https:\/\/doi.org\/10.1080\/01621459.2018.1429274","journal-title":"J. Am. Stat. Assoc."},{"key":"10548_CR12","unstructured":"Ju, J., Wang, M., Zhao, S.: Subsampling for big data linear models with measurement errors. arXiv:2403.04361 (2024)"},{"issue":"2","key":"10548_CR13","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1214\/aos\/1028144858","volume":"26","author":"K Knight","year":"1998","unstructured":"Knight, K.: Limiting distributions for $$l_1$$ regression estimators under general conditions. Ann. Stat. 26(2), 755\u2013770 (1998). https:\/\/doi.org\/10.1214\/aos\/1028144858","journal-title":"Ann. Stat."},{"issue":"1","key":"10548_CR14","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1051\/epjconf\/20100402005","volume":"20","author":"C Leng","year":"2010","unstructured":"Leng, C.: Variable selection and coefficient estimation via regularized rank regression. Stat. Sin. 20(1), 167\u2013181 (2010). https:\/\/doi.org\/10.1051\/epjconf\/20100402005","journal-title":"Stat. Sin."},{"key":"10548_CR15","unstructured":"Lid Hjort, N., Pollard, D.: Asymptotics for minimisers of convex processes. arXiv:1107.3806 (2011)"},{"key":"10548_CR16","doi-asserted-by":"publisher","first-page":"106976","DOI":"10.1016\/j.csda.2020.106976","volume":"149","author":"J Lee","year":"2020","unstructured":"Lee, J., Wang, H., Schifano, E.D.: Online updating method to correct for measurement error in big data streams. Comput. Stat. Data Anal. 149, 106976 (2020). https:\/\/doi.org\/10.1016\/j.csda.2020.106976","journal-title":"Comput. Stat. Data Anal."},{"key":"10548_CR17","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1007\/s10463-021-00803-5","volume":"74","author":"J Luan","year":"2021","unstructured":"Luan, J., Wang, H., Wang, K., Zhang, B.: Robust distributed estimation and variable selection for massive datasets via rank regression. Ann. Inst. Stat. Math. 74, 435\u2013450 (2021). https:\/\/doi.org\/10.1007\/s10463-021-00803-5","journal-title":"Ann. Inst. Stat. Math."},{"issue":"5","key":"10548_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11222-024-10477-7","volume":"34","author":"X Li","year":"2024","unstructured":"Li, X., Xia, X., Zhang, Z.: Distributed subsampling for multiplicative regression. Stat. Comput. 34(5), 1\u201320 (2024). https:\/\/doi.org\/10.1007\/s11222-024-10477-7","journal-title":"Stat. Comput."},{"key":"10548_CR19","doi-asserted-by":"publisher","unstructured":"Li, X., Xia, X., Zhang, Z.: Poisson subsampling-based estimation for growing-dimensional expectile regression in massive data. Stat. Comput. 34, 133 (2024). https:\/\/doi.org\/10.1007\/s11222-024-10449-x","DOI":"10.1007\/s11222-024-10449-x"},{"issue":"27","key":"10548_CR20","doi-asserted-by":"publisher","first-page":"861","DOI":"10.48550\/arXiv.1306.5362","volume":"16","author":"P Ma","year":"2015","unstructured":"Ma, P., Mahoney, M.W., Yu, B.: A statistical perspective on algorithmic leveraging. J. Mach. Learn. Res. 16(27), 861\u2013911 (2015). https:\/\/doi.org\/10.48550\/arXiv.1306.5362","journal-title":"J. Mach. Learn. Res."},{"key":"10548_CR21","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1214\/ss\/1030037960","volume":"12","author":"S Portnoy","year":"1997","unstructured":"Portnoy, S., Koenker, R.: The gaussian hare and the Laplacian tortoise: computability of squared- error versus absolute-error estimators. Stat. Sci. 12, 279\u2013296 (1997). https:\/\/doi.org\/10.1214\/ss\/1030037960","journal-title":"Stat. Sci."},{"key":"10548_CR22","doi-asserted-by":"publisher","first-page":"101694","DOI":"10.1016\/j.jco.2022.101694","volume":"74","author":"M Ren","year":"2023","unstructured":"Ren, M., Zhao, S., Wang, M.: Optimal subsampling for least absolute relative error estimators with massive data. J. Complex. 74, 101694 (2023). https:\/\/doi.org\/10.1016\/j.jco.2022.101694","journal-title":"J. Complex."},{"issue":"3","key":"10548_CR23","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1080\/00401706.2016.1142900","volume":"58","author":"ED Schifano","year":"2016","unstructured":"Schifano, E.D., Wu, J., Wang, C., Yan, J., Chen, M.-H.: Online updating of statistical inference in the big data setting. Technometrics 58(3), 393\u2013403 (2016). https:\/\/doi.org\/10.1080\/00401706.2016.1142900","journal-title":"Technometrics"},{"key":"10548_CR24","doi-asserted-by":"publisher","unstructured":"T\u00fcfekci, P.: Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods. Int. J. Electr. Power Energy Syst. 60, 126\u2013140 (2014). https:\/\/doi.org\/10.1016\/j.ijepes.2014.02.027","DOI":"10.1016\/j.ijepes.2014.02.027"},{"key":"10548_CR25","doi-asserted-by":"publisher","unstructured":"Wang, H.: More efficient estimation for logistic regression with optimal subsamples. J. Mach. Learn. Res. 20(132), 1\u201359 (2018). https:\/\/doi.org\/10.48550\/arXiv.1802.02698","DOI":"10.48550\/arXiv.1802.02698"},{"key":"10548_CR26","doi-asserted-by":"publisher","unstructured":"Wang, H., Ma, Y.: Optimal subsampling for quantile regression in big data. Biometrika 108(1), 99\u2013112 (2020). https:\/\/doi.org\/10.1093\/biomet\/asaa043","DOI":"10.1093\/biomet\/asaa043"},{"key":"10548_CR27","doi-asserted-by":"publisher","first-page":"1700","DOI":"10.1080\/01621459.2020.1840989","volume":"115","author":"L Wang","year":"2020","unstructured":"Wang, L., Peng, B., Bradic, J., Li, R., Wu, Y.: A tuning-free robust and efficient approach to high-dimensional regression (with discussion). J. Am. Stat. Assoc. 115, 1700\u20131714 (2020). https:\/\/doi.org\/10.1080\/01621459.2020.1840989","journal-title":"J. Am. Stat. Assoc."},{"issue":"522","key":"10548_CR28","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1080\/01621459.2017.1292914","volume":"113","author":"H Wang","year":"2018","unstructured":"Wang, H., Zhu, R., Ma, P.: Optimal subsampling for large sample logistic regression. J. Am. Stat. Assoc. 113(522), 829\u2013844 (2018). https:\/\/doi.org\/10.1080\/01621459.2017.1292914","journal-title":"J. Am. Stat. Assoc."},{"issue":"2","key":"10548_CR29","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1007\/s00362-022-01386-w","volume":"65","author":"J Yu","year":"2024","unstructured":"Yu, J., Ai, M., Ye, Z.: A review on design inspired subsampling for big data. Stat. Pap. 65(2), 467\u2013510 (2024). https:\/\/doi.org\/10.1007\/s00362-022-01386-w","journal-title":"Stat. Pap."},{"issue":"20","key":"10548_CR30","doi-asserted-by":"publisher","first-page":"911","DOI":"10.5705\/ss.202022.0020","volume":"34","author":"Y Yao","year":"2024","unstructured":"Yao, Y., Jin, Z.: A perturbation subsampling for large scale data. Stat. Sin. 34(20), 911\u2013932 (2024). https:\/\/doi.org\/10.5705\/ss.202022.0020","journal-title":"Stat. Sin."},{"issue":"1","key":"10548_CR31","doi-asserted-by":"publisher","first-page":"151","DOI":"10.6339\/21-JDS999","volume":"19","author":"Y Yao","year":"2021","unstructured":"Yao, Y., Wang, H.: A review on optimal subsampling methods for massive datasets. J. Data Sci. 19(1), 151\u2013172 (2021). https:\/\/doi.org\/10.6339\/21-JDS999","journal-title":"J. Data Sci."},{"key":"10548_CR32","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1080\/01621459.2020.1773832","volume":"117","author":"J Yu","year":"2020","unstructured":"Yu, J., Wang, H., Ai, M., Zhang, H.: Optimal distributed subsampling for maximum quasi-likelihood estimators with massive data. J. Am. Stat. Assoc. 117, 265\u2013276 (2020). https:\/\/doi.org\/10.1080\/01621459.2020.1773832","journal-title":"J. Am. Stat. Assoc."},{"issue":"546","key":"10548_CR33","doi-asserted-by":"publisher","first-page":"1500","DOI":"10.1080\/01621459.2023.2202433","volume":"119","author":"L Zhou","year":"2024","unstructured":"Zhou, L., Wang, B., Zou, H.: Sparse convoluted rank regression in high dimensions. J. Am. Stat. Assoc. 119(546), 1500\u20131512 (2024). https:\/\/doi.org\/10.1080\/01621459.2023.2202433","journal-title":"J. Am. Stat. Assoc."}],"container-title":["Statistics and Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11222-024-10548-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11222-024-10548-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11222-024-10548-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T18:15:14Z","timestamp":1737569714000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11222-024-10548-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,14]]},"references-count":33,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["10548"],"URL":"https:\/\/doi.org\/10.1007\/s11222-024-10548-9","relation":{},"ISSN":["0960-3174","1573-1375"],"issn-type":[{"value":"0960-3174","type":"print"},{"value":"1573-1375","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,14]]},"assertion":[{"value":"9 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"14"}}