A Comparison Between New Modification of ANWK and Classical ANWK Methods in Nonparametric Regression

A Simulation Study

  • Hazhar T. A. Blbas Department of Statistics, College of Administration and Economics, Salahaddin University-Erbil, ‎Kurdistan Region, Iraq ‎
  • Wasfi T. Kahwachi Research Center, Tishk International University-Erbil (TIU), Kurdistan Region, Iraq ‎ https://orcid.org/0000-0002-1610-2998
Keywords: Non-parametric Regression, Kernel Regression, New ANW Estimators, Leukemia Cancer, AML

Abstract

Nonparametric kernel estimators are mostly used in a variety of statistical research fields. Nadaraya-Watson kernel estimator (NWK) is one of the most important nonparametric kernel estimator that is often used in regression models with a fixed bandwidth. In this article, we consider the four new Proposed Adaptive Nadaraya-Watson Kernel Regression Estimators (Interquartile Range, Standard Deviation, Mean Absolute Devotion, and Median Absolute Deviation) rather than (Fixed Bandwidth, Adaptive Geometric, Adaptive Mean, Adaptive Range, and Adaptive Median). The outcomes in both simulation and actual data in Leukemia Cancer show that the four new ANW Kernel Estimators (Interquartile Range, Standard Deviation, Mean Absolute devotion, and Median Absolute Deviation) is more effective than the kernel estimations with fixed bandwidth in previous studies using Mean Square Error (MSE) Criterion.

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Published
2021-10-30
How to Cite
1.
Blbas H, Kahwachi W. A Comparison Between New Modification of ANWK and Classical ANWK Methods in Nonparametric Regression. cuesj [Internet]. 30Oct.2021 [cited 7Dec.2021];5(2):32-7. Available from: https://journals.cihanuniversity.edu.iq/index.php/cuesj/article/view/483
Section
Research Article