Author, Subjects, Keywords

Cited Author

 
 
   » By Author or Editor
 » Browse Author by Alphabet
 » By Journal
 » By Subjects
 » Malaysian Journals
 » By Type
 » By Year
 » By Latest Additions
 
 
   » By Author
 » Top 20 Authors
 » Top 20 Article
 » Top Journal Cited
 » Top Article Cited
 » Journal Citation Statistics
 » Usage Since Sept 2007


 
 
 

Login | Create Account

Multiple Outliers Detection Procedures in Linear Regression

Robiah Adnan, and Mohd Nor Mohamad, and Halim Setan, (2003) Multiple Outliers Detection Procedures in Linear Regression. Matematika, 19 (1). pp. 29-45. ISSN 01278274

[img]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
178Kb

Official URL: http://www.fs.utm.my/matematika/images/stories/matematika/200319105.pdf

Affiliations

Universiti Teknologi Malaysia, Dept. of Mathematics
Universiti Teknologi Malaysia, Dept. of Mathematics
Universiti Teknologi Malaysia, Faculty of Geoinformation Science and Engineering

Abstract

This paper describes a procedure for identifying multiple outliers in linear regression. This procedure uses a robust fit which is the least of trimmed of squares (LTS) and the single linkage clustering method to obtain the potential outliers. Then multiple-case diagnostics are used to obtain the outliers from these potential outliers. The performance of this procedure is also compared to Serbert’s method. Monte Carlo simulations are used in determining which procedure performed best in all of the linear regression scenarios. Keywords: Multiple outliers, linear regression, robust fit, Least trimmed of squares, single linkage.

Item Type:Journal
Keywords:Multiple outliers, linear regression, robust fit, least trimmed of squares, single linkage
Subjects:Q Science, Computer Science
ID Code:5723

[1] Barnett, V. and Lewis, T. (1994), Outliers in Statistical Data, 3rd ed., John Wiley: Great Britain.

[2] Belsley, D.A., Kuh, E. and Welsch, R.E. (1980), Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, Wiley: New York, NY.

[3] Burns, P.J.(1992), A genetic algorithm for robust regression estimation, StatSci Technical Note, Seattle, WA.

[4] Cook, R. D. (1979), Influential Observations in Linear Regression, Journal of the American Statistical Association, 74, 169-174.

[5] Cook, R.D. and Weisberg, S. (1982), Characterizations of an empirical influence function for detecting influential cases in regression, Technometrics, 22, 495-508.

[6] Everitt, B.S. (1993), Cluster Analysis, 3rd edition, New York: Halsted Press.

[7] Johnson, R.A. and Wichern, D.W. (1982), Applied Multivariate Statistical Analysis, 3rd edition, New Jersey : Prentice Hall.

[8] Mojena, R. (1977), Hierarchical grouping methods and stopping rules: An evaluation, The Computer Journal, 20, 359-363.

[9] Milligan G. W., and Cooper, M.C. (1985), An Examination of Procedures for Determining the Number of Clusters in a Data Set, Psychometrika, Vol. 50, No. 2, 159-179.

[10] Minowski, J.W. (1999), Multiple Outliers in Linear Regression: Advances in Detection Methods, Robust Estimation and Variable Selection, Unpublished dissertation, Arizona State University, Arizona.

[11] Rousseeuw, P.J. (1984), Least Median of Squares Regression, Journal of the American Statistical Association, 79, 871-880.

[12] Rousseeuw and Leroy (1987), Robust Regression and outlier detection, John Wiley & Sons, New York.

[13] Serbert, D.M. (1996), Identifying Multiple Outliers and Influential Subsets :A Clustering Approach, unpublished dissertation, Arizona State University, AZ.

[14] Serbert, D.M., Montgomery,D.C. and Rollier, D. (1998), A clustering algorithm for identifying multiple outliers in linear regression, Computational Statistics & Data Analysis, 27, 461-484.

[15] Woodruff, D.L. and Rocke,D.M. (1994), Computable Robust Estimation of Multivariate Shape in High Dimension Using Compound Estimators, Journal of the American Statistical Association. 89, 888-896.

[16] You, J. (1999), A Monte Carlo Comparison of Several High Breakdown and Efficient Estimators, Computational Statistics & Data Analysis. 30, 205-219.

Repository Staff Only: item control page
 
 
 
 
 
 
© Copyright 2007 MyAIS Faculty of Computer Science and Information Technology, University of Malaya – All Rights Reserved. Malaysian Abstracting and Indexing System is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.