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Journals / Anadolu Üniversitesi Bilim ve Teknoloji Dergisi :A-Uygulamalı Bilimler ve Mühendislik / 2000 / Cilt: 1 - Sayı: 1

A study on the Cp criteria applications in robust regression

Sağlam Cp kriterinin uygulanması üzerine bir çalışma

Candan Meral ÇETİN
Aydın ERAR
Pages
77–84
Publication Date
2022-10-11
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In this paper, the classical Cp criteria which depends on the Hubers-type and Mallows-type weight functions will be compared in the presence of outliers.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In multiple linear regression, outliers are very efficient on the variable selection.
📊
Key Findings & Data:This effects of the outliers are removed by the Hubers-type and Mallows-type etc. weight functions.
💡
Conclusion & Impact:In this paper, the classical Cp criteria which depends on the Hubers-type and Mallows-type weight functions will be compared in the presence of outliers.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

In multiple linear regression, outliers are very efficient on the variable selection. This effects of the outliers are removed by the Hubers-type and Mallows-type etc. weight functions. In this paper, the classical Cp criteria which depends on the Hubers-type and Mallows-type weight functions will be compared in the presence of outliers.

Özet

Çoklu doğrusal regresyonda, aykırı değerler (outliers), değişken seçiminde büyük bir etkiye sahiptirler. Aykırı değerlerin bu etkisi, Huber-türü, Mallows-türü vb. ağırlık fonksiyonları ile giderilir. Bu çalışmada, değişken seçimi üzerinde durularak, aykıri değer varlığında klasik Cp kriteri, Huber ve Mallows-türü ağırlık fonksiyonlarına dayalı sağlam (robust) Cp kriterleri ile karşılıştırılacaktır.

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Cite This Article

ÇETİN, C. M., ERAR, A. (2022). A study on the Cp criteria applications in robust regression. Anadolu Üniversitesi Bilim ve Teknoloji Dergisi :A-Uygulamalı Bilimler ve Mühendislik, Cilt: 1 - Sayı: 1, 77–84. https://ajindex.com/articles/438537

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References (12)

  1. Akaike. H. (1974). A new look at the statistical model identification, IEEE Transaction on Automatic Control, AC-19,716-723.
  2. Coakley, C.W. and Hettmansperger, TP. (1993),
  3. A Bounded Influence High Breakdown, Efficient Regression Estimator, J ASA, 88, 872-880. Hampel. F.R., (1983), Some aspects of model choice in robust statistics, hi Proc. 44'h Sess. International Statistics Institute. Madrid, book 2, 767-771.
  4. Hampel. F.R., Ronchetti, E.M., Rousseeuw, P.J., and Stahcl. W.A., (1986), Robust Statistics: The Approach Based on Influence Functions, John Wiley, New York. Hurvich, C.F. and Tsai, C.L., (1989), Regression and time series model selection in small samples, Ri-omerrika, 76,2, 297-307.
  5. Hurvich, C.F. and Tsai, C.L., (1990), Model selection for least absolute deviations regression in small samples, Statistics and Probab. Letters. 9, 259-265. Machado,J.A.F., (1993), Robust model selection and M estimation, Econometric Theory, 9,478-493.
  6. Martin, R.D., (1980), Robust Estimation of Autoregres-sive Models, In Direction in Time Series, Institute of Mathematical Statistics, 228-262.
  7. Ryan, P.T., (1998),
  8. Modern Regression, John Wiley, New York. Ronchetti, E., (1982), Robust Testing in linear models; the infinitesimal approach, PhD Thesis.,Eidgenössische Technisches Hochschule, Zurich. Ronchetti, E., (1985),
  9. Robust model selection in regression, Statistics and Probab. Letters., 3, 21-23.
  10. Ronchetti, E., Field, C. and Blanchard, W., (1997), Robust linear model selection by cross- validation, J ASA, 92, N.439, 1917-1924.
  11. Ronchetti, E. and Staudte, R.G., (1994), A robust versions of Mallows1 Cp, J Am. Statst. Ass., 89. 550-559.
  12. Staudte. R.G. and Sheather, S J., (1990), Robust Estimation and Testing. John Wiley, New York.