alphanumeric journal
The Journal of Operations Research, Statistics, Econometrics and Management Information Systems
Comparison of Parametric and Non-Parametric Estimation Methods in Linear Regression Model
Abstract
In this study, the aim was to review the methods of parametric and non-parametric analyses in simple linear regression model. The least squares estimator (LSE) in parametric analysis of the model, and Mood-Brown and Theil-Sen methods that estimates the parameters according to the median value in non-parametric analysis of the model are introduced. Also, various weights of Theil-Sen method are examined and estimators are discussed. In an attempt to show the need for non-parametric methods, results are evaluated based on real life data.
Keywords: Least Squares, Mean Absolute Deviation, Median, Mood-Brown Estimator, Outlier, Theil-Sen Estimator
Jel Classification: C40
Doğrusal Regresyon Modelinde Parametrik ve Parametrik Olmayan Tahmin Yöntemlerinin Karşılaştırması
Öz
Bu çalışmada, basit doğrusal regresyon modelinde parametrik ve parametrik olmayan analiz yöntemlerinin karşılaştırmalı olarak incelenmesi amaçlanmıştır. Modelin parametrik analizinde EKK tahmini, parametrik olmayan analizinde ise medyana göre parametre tahmini yapan Mood-Brown ve Theil-Sen yöntemleri tanıtılmıştır. Ayrıca Theil-Sen yöntemine ait çeşitli ağırlıklar incelenerek parametre tahmin ediciler tartışılmıştır. Parametrik olmayan yöntemlere olan ihtiyacı göstermek amacı ile sonuçlar gerçek yaşam verisi üzerinde değerlendirilmiştir.
Anahtar Kelimeler: Aykırı Değerler, En Küçük Kareler Metodu, Medyan, Mood-Brown Tahmini, Ortalama Mutlak Sapma, Theil-Sen Tahmini
Suggested citation
(). Comparison of Parametric and Non-Parametric Estimation Methods in Linear Regression Model. Alphanumeric Journal, 7(1), 13-24. http://dx.doi.org/10.17093/alphanumeric.346469
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2019.07.01.STAT.02
alphanumeric journal
Pages 13-24
Received: Oct. 25, 2017
Accepted: March 22, 2019
Published: June 30, 2019
2019 Zaman, T., Alakuş, K.
This is an Open Access article, licensed under Creative Commons Attribution-NonCommercial 4.0 International License.
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