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Some Robust Estimation Methods and Their Applications

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  • Tolga Zaman
  • Kamil Alakuş

Abstract

This study examines robust regression methods which are used for the solution of problems caused by the situations in which the assumptions of LSM technique, which is commonly used for the prediction of linear regression models, cannot be used. Robust estimators are not influenced by small deviations and discrepancies. For this purpose, some robust regression techniques which are used in situations in which the assumptions cannot be made were introduced and parameter estimation algorithms of these techniques were analyzed. Regression models of the methods of Lad, Weighted –M regression, Theil regression and Least Median Squares, coefficients of determination and average absolute deviations were calculated and the results were discussed as to which of these methods gave better results.

Suggested Citation

  • Tolga Zaman & Kamil Alakuş, 2016. "Some Robust Estimation Methods and Their Applications," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 3(2), pages 73-82, December.
    • Handle: RePEc:anm:alpnmr:v:3:y:2016:i:2:p:73-82
    DOI: http://dx.doi.org/10.17093/aj.2015.3.2.5000152703
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    References listed on IDEAS

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    1. Wilcox, Rand R., 1998. "Simulations on the Theil-Sen regression estimator with right-censored data," Statistics & Probability Letters, Elsevier, vol. 39(1), pages 43-47, July.
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    Cited by:

    1. Tolga Zaman & Kamil Alakuş, 2019. "Comparison of Parametric and Non-Parametric Estimation Methods in Linear Regression Model," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 7(1), pages 13-24, June.

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    More about this item

    Keywords

    Average Absolute Deviations; Coefficient of Determination; Least Square Errors Methods; Robust Regression Methods;
    All these keywords.

    JEL classification:

    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General

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