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Communications in Statistics - Theory and Methods Volume 19, 1990 - Issue 5
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Original Articles

A monte carlo comparison of five procedures for identifying outliers in linear regression

Farid Kianifard Department of Mathematics, Plymouth State College, Plymouth, New Hampshire, 03264, U.S.A
&
William H. Swallow Department of Statistics, North Carolina State University, Raleigh, North Carolina, 27695-8203, U.S.A.
Pages 1913-1938 | Received 01 Jun 1989, Published online: 27 Jun 2007

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Ekele Alih & Hong Choon Ong. (2015) Cluster-based multivariate outlier identification and re-weighted regression in linear models. Journal of Applied Statistics 42:5, pages 938-955.
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Nedret Billor & Gulsen Kiral. (2008) A Comparison of Multiple Outlier Detection Methods for Regression Data. Communications in Statistics - Simulation and Computation 37:3, pages 521-545.
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Bill Seaver, Konstantinos Triantis & Chip Reeves. (1999) The Identification of Influential Subsets in Regression Using a Fuzzy Clustering Strategy. Technometrics 41:4, pages 340-351.
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M. Mercedes Suárez Rancel & Miguel A. Gonzalez Sierra.. (1999) Measures and procedures for the identification of locally influential observations in linear regression. Communications in Statistics - Theory and Methods 28:2, pages 343-366.
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Farid Kianifard & WilliamH. Swallow. (1996) A Review of the Development and Application of Recursive Residuals in Linear Models. Journal of the American Statistical Association 91:433, pages 391-400.
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AliS. Hadi & JeffreyS. Simonoff. (1993) Procedures for the Identification of Multiple Outliers in Linear Models. Journal of the American Statistical Association 88:424, pages 1264-1272.
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Mariusz Maziarz. (2022) Is meta-analysis of RCTs assessing the efficacy of interventions a reliable source of evidence for therapeutic decisions?. Studies in History and Philosophy of Science 91, pages 159-167.
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Han Son Seo & Min Yoon. (2021) Least quantile squares method for the detection of outliers. Communications for Statistical Applications and Methods 28:1, pages 81-88.
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Brenton R. Clarke. 2018. Robustness Theory and Application. Robustness Theory and Application 195 209 .
Han Son Seo & Min Yoon. (2016) Robust tests for heteroscedasticity using outlier detection methods. Korean Journal of Applied Statistics 29:3, pages 399-408.
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Sudhir R. Paul. 2014. Wiley StatsRef: Statistics Reference Online. Wiley StatsRef: Statistics Reference Online.
Han Son Seo & Min Yoon. (2014) A Test on a Specific Set of Outlier Candidates in a Linear Model. Korean Journal of Applied Statistics 27:2, pages 307-315.
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Qingjiang Hou, Brandon Crosser, Jonathan D Mahnken, Byron J Gajewski & Nancy Dunton. (2012) Input data quality control for NDNQI national comparative statistics and quarterly reports: a contrast of three robust scale estimators for multiple outlier detection. BMC Research Notes 5:1.
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Han-Son Seo & Min Yoon. (2012) Clustering Observations for Detecting Multiple Outliers in Regression Models. Korean Journal of Applied Statistics 25:3, pages 503-512.
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Pradeep B. Kunda, Fernando Benavente, Sergio Catalá-Clariana, Estela Giménez, José Barbosa & Victoria Sanz-Nebot. (2012) Identification of bioactive peptides in a functional yogurt by micro liquid chromatography time-of-flight mass spectrometry assisted by retention time prediction. Journal of Chromatography A 1229, pages 121-128.
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Byung-Jin Ahn & Han-Son Seo. (2011) Outlier Detection Using Dynamic Plots. Korean Journal of Applied Statistics 24:5, pages 979-986.
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Daniel Peña. 2006. Springer Handbook of Engineering Statistics. Springer Handbook of Engineering Statistics 523 536 .
Bu-Yong Kim & Mi-Hyun Oh. (2004) Identification of Regression Outliers Based on Clustering of LMS-residual Plots. Communications for Statistical Applications and Methods 11:3, pages 485-494.
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Bu-yong Kim & Hee-young Kim. (2002) A Hybrid Algorithm for Identifying Multiple Outlers in Linear Regression. Communications for Statistical Applications and Methods 9:1, pages 291-304.
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Sudhir R. Paul. 2001. Encyclopedia of Environmetrics. Encyclopedia of Environmetrics.
James W Wisnowski, Douglas C Montgomery & James R Simpson. (2001) A Comparative analysis of multiple outlier detection procedures in the linear regression model. Computational Statistics & Data Analysis 36:3, pages 351-382.
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M.F Jiang, S.S Tseng & C.M Su. (2001) Two-phase clustering process for outliers detection. Pattern Recognition Letters 22:6-7, pages 691-700.
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Chih-Ming Su, Shian-Shyong Tseng, Monn-Fong Jiang & Joe C. S. Chen. 1999. Methodologies for Knowledge Discovery and Data Mining. Methodologies for Knowledge Discovery and Data Mining 360 364 .
David M. Sebert, Douglas C. Montgomery & Dwayne A. Rollier. (1998) A clustering algorithm for identifying multiple outliers in linear regression. Computational Statistics & Data Analysis 27:4, pages 461-484.
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Daniel Peña & Victor J. Yohai. (1995) The Detection of Influential Subsets in Linear Regression by Using an Influence Matrix. Journal of the Royal Statistical Society: Series B (Methodological) 57:1, pages 145-156.
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