Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2011, Cilt: 24 Sayı: 1, 59 - 68, 14.01.2011

Öz

Kaynakça

  • Belsley, D.A. , Kuh, E. Welsch, R., “Regression Diagnostics”. Wiley and Sons, N.Y. and Toronto. (1980).
  • Billor, N., Chatterjee, S., Hadi, A.S., “A Re- Weighted Least Squares Method for Robust Regression Estimation”, American Journal of Mathematical and Management Sciences, 26, Nos. 3&4, 229-252 (2006).
  • Billor, N., Kiral, G., “A comparison of multiple outlier detection methods for regression data”, Comm. in Stat., 37, 521-545 (2008).
  • Cook, R.D., Weisberg, S., “Residuals and Influence in Regression. London: Chapman and Hall (1982).
  • Hadi, A.S., “Identifying multiple outliers in multivariate data”, J. Roy. Statist. Soc. Ser. B 54, 761–777 (1992).
  • Hadi, A.S., Simonoff, J.S., Procedures for the identification of multiple outliers in linear models, J. Amer. Statist. Assoc., 88, 1264 –1272 (1993).
  • Hadi, A.S., “A modification of a method for the detection of outliers in multivariate samples”, J. Roy. Statist. Soc. Ser. B 56, 393–396 (1994).
  • Kianifard, F., Swallow, W., A Monte Carlo comparison of some procedures for identifying outliers in linear regression, Commun. Statist. Part A Theory Methods, 19, 1913–1938 (1990).
  • Kim, S., Krzanowski, W.J., , Detecting multiple outliers in linear regression using a cluster method combined with graphical visualization, Comp. Stat., 22, 109-119 2007
  • Marchette, D.J., Solka, J.L., 2003, Using Data Images for Outlier Detection, Comput. Statist. Data Anal., 43, 541-552.
  • McCullough, B. D., 1998 Assessing the reliability of statistical software: Part I. The American Statistician 52, 358–366.
  • McCullough, B. D., 1999, “Assessing the reliability of statistical software: Part II”. The American Statistician 53, 149-159.
  • McCullough, B. D., 2000,The Accuracy of Mathematica 4 as a Statistical Package,, Computational Statistics 15 , 279-299.
  • McCullough, B. D. , Heiser, D. A., 2008, On the accuracy of statistical procedures in Microsoft Excel 2007, Computational Statistics and Data Analysis 52, 4570-4578.
  • Pena, D., Yahoi, V.J., 1995, The detection of Influential Subsets in Linear Regression by Using an Influence Matrix, J. Roy. Statist. Soc. Ser. B 57(1), 145-156.
  • Rousseeuw, P.J, 1984, Least median of squares regression, J. Amer. Statist. Assoc. 79, 871-881.
  • Rousseeuw,P.J, 1985,Multivariate Estimation with
  • High Breakdown Point, Mathematical Statistics and Applications, ed. by W. Grossmann, G. Pflug, I. Vincze, and W. Wertz, Dordrecht: Reidel Publishing Company, 283-297.
  • Rousseeuw, P.J., Leroy, A.M., 1987, “Robust Regression and Outlier Detection”, Wiley, New York.
  • Rousseeuw, P.J, Van Zomeren, B.C., 1990, Unmasking multivariate outliers and leverage points, J. Amer. Statist. Assoc., 85, 633-639.
  • Rousseeuw,P.J, Huber,M,1997, URL. http://www.agoras.ua.ac.be/abstract/Recdev97.htm
  • Sebert, D.M., Montgomery, D.C., Rollier, D., 1998, A clustering algorithm for identifying multiple outliers in linear regression, Comput. Statist. Data Anal. 27, 461-484.
  • Wisnowski, J.W. , Montgomery, D.C., Simpson, J.R., 2001, “A comparative analysis of multiple outlier detection procedures in the linear regression model”, Comput. Statist. Data Anal., 36, 351–382.

PURO: A Package for Unmasking Regression Outliers

Yıl 2011, Cilt: 24 Sayı: 1, 59 - 68, 14.01.2011

Öz

Multiple regression outliers should be identified because of their potential effect on parameter estimates and inferences from the regression model. In recent years, researchers have proposed numerous strategies and procedures to identify the outliers. A Mathematica package PURO is introduced which implements seven methods from the latest and most respected outlier detection procedures in the statistics literature.

Kaynakça

  • Belsley, D.A. , Kuh, E. Welsch, R., “Regression Diagnostics”. Wiley and Sons, N.Y. and Toronto. (1980).
  • Billor, N., Chatterjee, S., Hadi, A.S., “A Re- Weighted Least Squares Method for Robust Regression Estimation”, American Journal of Mathematical and Management Sciences, 26, Nos. 3&4, 229-252 (2006).
  • Billor, N., Kiral, G., “A comparison of multiple outlier detection methods for regression data”, Comm. in Stat., 37, 521-545 (2008).
  • Cook, R.D., Weisberg, S., “Residuals and Influence in Regression. London: Chapman and Hall (1982).
  • Hadi, A.S., “Identifying multiple outliers in multivariate data”, J. Roy. Statist. Soc. Ser. B 54, 761–777 (1992).
  • Hadi, A.S., Simonoff, J.S., Procedures for the identification of multiple outliers in linear models, J. Amer. Statist. Assoc., 88, 1264 –1272 (1993).
  • Hadi, A.S., “A modification of a method for the detection of outliers in multivariate samples”, J. Roy. Statist. Soc. Ser. B 56, 393–396 (1994).
  • Kianifard, F., Swallow, W., A Monte Carlo comparison of some procedures for identifying outliers in linear regression, Commun. Statist. Part A Theory Methods, 19, 1913–1938 (1990).
  • Kim, S., Krzanowski, W.J., , Detecting multiple outliers in linear regression using a cluster method combined with graphical visualization, Comp. Stat., 22, 109-119 2007
  • Marchette, D.J., Solka, J.L., 2003, Using Data Images for Outlier Detection, Comput. Statist. Data Anal., 43, 541-552.
  • McCullough, B. D., 1998 Assessing the reliability of statistical software: Part I. The American Statistician 52, 358–366.
  • McCullough, B. D., 1999, “Assessing the reliability of statistical software: Part II”. The American Statistician 53, 149-159.
  • McCullough, B. D., 2000,The Accuracy of Mathematica 4 as a Statistical Package,, Computational Statistics 15 , 279-299.
  • McCullough, B. D. , Heiser, D. A., 2008, On the accuracy of statistical procedures in Microsoft Excel 2007, Computational Statistics and Data Analysis 52, 4570-4578.
  • Pena, D., Yahoi, V.J., 1995, The detection of Influential Subsets in Linear Regression by Using an Influence Matrix, J. Roy. Statist. Soc. Ser. B 57(1), 145-156.
  • Rousseeuw, P.J, 1984, Least median of squares regression, J. Amer. Statist. Assoc. 79, 871-881.
  • Rousseeuw,P.J, 1985,Multivariate Estimation with
  • High Breakdown Point, Mathematical Statistics and Applications, ed. by W. Grossmann, G. Pflug, I. Vincze, and W. Wertz, Dordrecht: Reidel Publishing Company, 283-297.
  • Rousseeuw, P.J., Leroy, A.M., 1987, “Robust Regression and Outlier Detection”, Wiley, New York.
  • Rousseeuw, P.J, Van Zomeren, B.C., 1990, Unmasking multivariate outliers and leverage points, J. Amer. Statist. Assoc., 85, 633-639.
  • Rousseeuw,P.J, Huber,M,1997, URL. http://www.agoras.ua.ac.be/abstract/Recdev97.htm
  • Sebert, D.M., Montgomery, D.C., Rollier, D., 1998, A clustering algorithm for identifying multiple outliers in linear regression, Comput. Statist. Data Anal. 27, 461-484.
  • Wisnowski, J.W. , Montgomery, D.C., Simpson, J.R., 2001, “A comparative analysis of multiple outlier detection procedures in the linear regression model”, Comput. Statist. Data Anal., 36, 351–382.
Toplam 23 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Statistics
Yazarlar

Enis Sınıksaran

Enis Sınıksaran

Mehmet Satman

Yayımlanma Tarihi 14 Ocak 2011
Yayımlandığı Sayı Yıl 2011 Cilt: 24 Sayı: 1

Kaynak Göster

APA Sınıksaran, E., Sınıksaran, E., & Satman, M. (2011). PURO: A Package for Unmasking Regression Outliers. Gazi University Journal of Science, 24(1), 59-68.
AMA Sınıksaran E, Sınıksaran E, Satman M. PURO: A Package for Unmasking Regression Outliers. Gazi University Journal of Science. Ocak 2011;24(1):59-68.
Chicago Sınıksaran, Enis, Enis Sınıksaran, ve Mehmet Satman. “PURO: A Package for Unmasking Regression Outliers”. Gazi University Journal of Science 24, sy. 1 (Ocak 2011): 59-68.
EndNote Sınıksaran E, Sınıksaran E, Satman M (01 Ocak 2011) PURO: A Package for Unmasking Regression Outliers. Gazi University Journal of Science 24 1 59–68.
IEEE E. Sınıksaran, E. Sınıksaran, ve M. Satman, “PURO: A Package for Unmasking Regression Outliers”, Gazi University Journal of Science, c. 24, sy. 1, ss. 59–68, 2011.
ISNAD Sınıksaran, Enis vd. “PURO: A Package for Unmasking Regression Outliers”. Gazi University Journal of Science 24/1 (Ocak 2011), 59-68.
JAMA Sınıksaran E, Sınıksaran E, Satman M. PURO: A Package for Unmasking Regression Outliers. Gazi University Journal of Science. 2011;24:59–68.
MLA Sınıksaran, Enis vd. “PURO: A Package for Unmasking Regression Outliers”. Gazi University Journal of Science, c. 24, sy. 1, 2011, ss. 59-68.
Vancouver Sınıksaran E, Sınıksaran E, Satman M. PURO: A Package for Unmasking Regression Outliers. Gazi University Journal of Science. 2011;24(1):59-68.