Journals / Hacettepe Journal of Mathematics and Statistics / 2026 / Volume: 41 Issue: 1

Outlier Detection by Regression Diagnostics Based on Robust Parameter Estimates FULL TEXT

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2012-01-01
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Türkan, S., Çetin, M. C., Toktamış, Ö. (2012). Outlier Detection by Regression Diagnostics Based on Robust Parameter Estimates FULL TEXT. Hacettepe Journal of Mathematics and Statistics, Volume: 41 Issue: 1. https://ajindex.com/articles/1403584

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References (16)

  1. Andrews, D. F. and Pregibon, D. Finding the outliers that matter, J. Roy. Statist. Soc. Ser. B 40, 85–93, 1978.
  2. Atkinson, A. C. Two graphical displays for outlying and influential observations in regres- sion, Biometrika 68, 13–20, 1981.
  3. Beckman, R. J. and Cook, R. D. Outlier. s, Technometrics 25 (2), 119–149, 1983.
  4. Belsley, D. A., Kuh, E. and Welsch, R. E. Regression Diagnostics: Identifying Influential Data and Sources of Collinearity(Wiley, New York, 1980).
  5. Cetin, M. Robust model selection criteria for robust Liu estimator, European Journal of Operational Research 199, 21–24, 2009.
  6. Chatterjee, S.and Hadi, A. S. Sensitivity Analysis in Linear Regression (Wiley Series in Probability and Mathematical Statistics, Wiley, New York, 1988).
  7. Chen, C. Robust regression and outlier detection with the ROBUSTREG procedure (Pro- ceedings of the Twenty-Seventh Annual SAS Users Group International Conference, Cary, NC: SAS Institute Inc, 2002).
  8. Cook, R. D. Detection of influential observations in linear regression, Technometrics 19, 15–18, 1977. [9] Cook, R. D. and Weisberg, S. Residuals and Influence in Regression (Chapman & Hall, New York, 1982). [10] Hadi, A.S. A new measure of overall potential influence in linear regression, Computational Statistics and Data Analysis 14, 1–27, 1992.
  9. Hoaglin, D. C, and Welsch, R. E. The Hat matrix in regression and ANOVA, The Amer. Statist. 32, 17–22, 1978.
  10. Huber, P. J. Robust Statistics (John Wiley & Sons, New York, 1981).
  11. Nurunnabi, A. A. M. and Nasser, M. Outlier detection by regression diagnostics in large data (International Conference on Future Computer and Communication, 2009), 246-250.
  12. Rawlings, J. O., Pantula, S. G. and Dickey, D. A. Applied Regression Analysis: A Research Tool(Springer, New York, 1998).
  13. Riazoshams, A. H., Habshah, B. and Adam, C. M. B. On the outlier detection in nonlinear regression, Engineering and Technology 60, 264–270, 2009.
  14. Ullah, M. A. and Pasha, G. R. The origin and developments of influence measures in re- gression, Pakistan Journal of Statistics 25 (3), 295–307, 2009.
  15. Welsch, R. E. and Kuh, E. Linear Regression Diagnostics (Technical report 923-77, Solan School of Management, Massachusetts Institute of Technology, 1977).
  16. Beckman, R. J. and Cook, R. D. Outlier. s, Technometrics 25 (2), 119–149, 1983.