{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T19:25:05Z","timestamp":1782501905099,"version":"3.54.5"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T00:00:00Z","timestamp":1676851200000},"content-version":"vor","delay-in-days":1480,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11871104 and 11471030"],"award-info":[{"award-number":["11871104 and 11471030"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Turing Fellowship under the Engineering and Physical Sciences Research Council","award":["EP\/N510129\/1"],"award-info":[{"award-number":["EP\/N510129\/1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,4,1]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>Influence diagnosis is an integrated component of data analysis but has been severely underinvestigated in a high dimensional regression setting. One of the key challenges, even in a fixed dimensional setting, is how to deal with multiple influential points that give rise to masking and swamping effects. The paper proposes a novel group deletion procedure referred to as multiple influential point detection by studying two extreme statistics based on a marginal-correlation-based influence measure. Named the min- and max-statistics, they have complementary properties in that the max-statistic is effective for overcoming the masking effect whereas the min-statistic is useful for overcoming the swamping effect. Combining their strengths, we further propose an efficient algorithm that can detect influential points with a prespecified false discovery rate. The influential point detection procedure proposed is simple to implement and efficient to run and enjoys attractive theoretical properties. Its effectiveness is verified empirically via extensive simulation study and data analysis. An R package implementing the procedure is freely available.<\/jats:p>","DOI":"10.1111\/rssb.12311","type":"journal-article","created":{"date-parts":[[2019,2,2]],"date-time":"2019-02-02T05:25:31Z","timestamp":1549085131000},"page":"385-408","source":"Crossref","is-referenced-by-count":15,"title":["Multiple Influential Point Detection in High Dimensional Regression Spaces"],"prefix":"10.1093","volume":"81","author":[{"given":"Junlong","family":"Zhao","sequence":"first","affiliation":[{"name":"Beijing Normal University \u00a0 People\u2019s Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Liu","sequence":"additional","affiliation":[{"name":"Beihang University \u00a0 People\u2019s Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Niu","sequence":"additional","affiliation":[{"name":"Beihang University \u00a0 People\u2019s Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenlei","family":"Leng","sequence":"additional","affiliation":[{"name":"University of Warwick, Coventry, and Alan Turing Institute , London , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2019,2,1]]},"reference":[{"key":"2023022012281406600_","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1145\/376284.375668","article-title":"Outlier detection for high dimensional data","volume":"30","author":"Aggarwal","year":"2001","journal-title":"ACM Sigmod Rec."},{"key":"2023022012281406600_","doi-asserted-by":"crossref","DOI":"10.1002\/0471725153","volume-title":"Regression Diagnostics: Identifying Influential Data and Sources of Collinearity","author":"Belsley","year":"1980"},{"key":"2023022012281406600_","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. 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