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TR
A Robust Initial Basic Subset Selection Method for Outlier Detection Algorithms in Linear Regression
Öz
The main motivation of this study is to develop an efficient algorithm for diagnosing and detecting outliers in linear regression up to a reasonable level of contamination. The algorithm initially obtains a robust version of the hat matrix at the linear algebra level. The basic subset obtained in the first stage is improved through concentration steps as defined in the fast-LTS (Least Trimmed Squares) regression algorithm. The method can be plugged into another algorithm as a basic subset selection state. The algorithm is effective against outliers in both X and Y directions by a rate of 25%. The complexity of the algorithm increases linearly with the number of observations and parameters. The algorithm is quite fast as it does not require iterative calculations. The success of the algorithm against a specific contamination level is demonstrated through simulations.
Anahtar Kelimeler
Kaynakça
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- [2] P. J. Rousseeuw and K. Van Driessen, “Computing LTS regression for large data sets,” Data mining and knowledge discovery, vol. 12, pp. 29–45, 2006.
- [3] D. C. Hoaglin and R. E. Welsch, “The hat matrix in regression and anova,” The American Statistician, vol. 32, no. 1, pp. 17–22, 1978.
- [4] J. W. Tukey et al., Exploratory data analysis. Reading, MA, 1977, vol. 2.
- [5] A. S. Hadi and J. S. Simonoff, “Procedures for the identification of multiple outliers in linear models,” Journal of the American statistical association, vol. 88, no. 424, pp. 1264–1272, 1993
- [6] N. Billor, A. S. Hadi, and P. F. Velleman, “Bacon: blocked adaptive computationally efficient outlier nominators,” Computational statistics & data analysis, vol. 34, no. 3, pp. 279–298, 2000.
- [7] N. Billor, S. Chatterjee, and A. S. Hadi, “A re-weighted least squares method for robust regression estimation,” American journal of mathematical and management sciences, vol. 26, no. 3-4, pp. 229–252, 2006.
- [8] D. A. Belsley, E. Kuh, and R. E. Welsch, Regression diagnostics: Identifying influential data and sources of collinearity. John Wiley & Sons, 2005.
Ayrıntılar
Birincil Dil
İngilizce
Konular
Ekonometrik ve İstatistiksel Yöntemler
Bölüm
Araştırma Makalesi
Yazarlar
Erken Görünüm Tarihi
24 Aralık 2024
Yayımlanma Tarihi
31 Aralık 2024
Gönderilme Tarihi
8 Temmuz 2024
Kabul Tarihi
16 Aralık 2024
Yayımlandığı Sayı
Yıl 2024 Sayı: 10
APA
Satman, M. H. (2024). A Robust Initial Basic Subset Selection Method for Outlier Detection Algorithms in Linear Regression. Journal of Statistics and Applied Sciences, 10, 76-85. https://doi.org/10.52693/jsas.1512794
AMA
1.Satman MH. A Robust Initial Basic Subset Selection Method for Outlier Detection Algorithms in Linear Regression. JSAS. 2024;(10):76-85. doi:10.52693/jsas.1512794
Chicago
Satman, Mehmet Hakan. 2024. “A Robust Initial Basic Subset Selection Method for Outlier Detection Algorithms in Linear Regression”. Journal of Statistics and Applied Sciences, sy 10: 76-85. https://doi.org/10.52693/jsas.1512794.
EndNote
Satman MH (01 Aralık 2024) A Robust Initial Basic Subset Selection Method for Outlier Detection Algorithms in Linear Regression. Journal of Statistics and Applied Sciences 10 76–85.
IEEE
[1]M. H. Satman, “A Robust Initial Basic Subset Selection Method for Outlier Detection Algorithms in Linear Regression”, JSAS, sy 10, ss. 76–85, Ara. 2024, doi: 10.52693/jsas.1512794.
ISNAD
Satman, Mehmet Hakan. “A Robust Initial Basic Subset Selection Method for Outlier Detection Algorithms in Linear Regression”. Journal of Statistics and Applied Sciences. 10 (01 Aralık 2024): 76-85. https://doi.org/10.52693/jsas.1512794.
JAMA
1.Satman MH. A Robust Initial Basic Subset Selection Method for Outlier Detection Algorithms in Linear Regression. JSAS. 2024;:76–85.
MLA
Satman, Mehmet Hakan. “A Robust Initial Basic Subset Selection Method for Outlier Detection Algorithms in Linear Regression”. Journal of Statistics and Applied Sciences, sy 10, Aralık 2024, ss. 76-85, doi:10.52693/jsas.1512794.
Vancouver
1.Mehmet Hakan Satman. A Robust Initial Basic Subset Selection Method for Outlier Detection Algorithms in Linear Regression. JSAS. 01 Aralık 2024;(10):76-85. doi:10.52693/jsas.1512794