Research Article

Comparison of Parametric and Non-Parametric Estimation Methods in Linear Regression Model

Volume: 7 Number: 1 June 30, 2019
EN TR

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

References

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  4. Hussain, S.S., and Sprent, P., (1983). Non Parametric Regression. J.Roy Statist. Soc., Series A, 146, 182-191.
  5. Kıroğlu, G. (2001). Uygulamalı Parametrik Olmayan İstatistiksel Yöntemler. Mimar Sinan Üniversitesi Fen-Edebiyat Fakültesi, İstanbul.
  6. Lehmann, E. L. (2006). Nonparametrics: Statistical methods based on ranks. New‐York: Springer pp. 463.
  7. Maritz, J.S., (1979). On Theil’s Method in Distribution-Free Regression. Australian J. of Statistics, 21, 30-35.
  8. Mood, A.M., (1950). Introduction to the Theory of Statistics, New York: Mcgraw- Hill.

Details

Primary Language

English

Subjects

-

Journal Section

Research Article

Authors

Tolga Zaman *
ÇANKIRI KARATEKİN ÜNİVERSİTESİ
0000-0001-8780-3655
Türkiye

Kamil Alakuş
Ondokuz Mayıs University
Türkiye

Publication Date

June 30, 2019

Submission Date

October 25, 2017

Acceptance Date

March 22, 2019

Published in Issue

Year 2019 Volume: 7 Number: 1

APA
Zaman, T., & Alakuş, K. (2019). Comparison of Parametric and Non-Parametric Estimation Methods in Linear Regression Model. Alphanumeric Journal, 7(1), 13-24. https://doi.org/10.17093/alphanumeric.346469

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