NONPARAMETRIC ADAPTIVE SMOOTHING WITH PRACTICAL APPLICATION

Authors

Noor Salah Hassan Department of Statistics, College of Administration and Economics, Basra University, Basra, Iraq
Sahera Hussein Zain Al-Thalabi Department of Statistics, College of Administration and Economics, Basra University, Basra, Iraq

Keywords:

Adaptive estimation, nonparametric regression, kernel and spline smoothing, bandwidth

Abstract

The researcher faces several when estimating the nonparametric regression functions because the estimation methods depend on the data, as these estimates may be inaccurate, or they may not be suitable for the nonparametric model , Therefore, the aim of the research is to find the adaptive estimators in the nonparametric regression through the adaptive bandwidth method, which is known as "Goldenshluger-lepski" to increase the estimation efficiency in the nonparametric regression.. In this paper, adaptive estimations were processed in the nonparametric regression method through the use of kernel smoothing and spline. The adaptive "Goldenshluger-Lepski" was included, and to compare the estimation methods three criteria were used, namely (MSE , MAS, RMSE) to choose the best method after applying the procedure to the simulation in the R Package

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Published

2022-09-28

How to Cite

Noor Salah Hassan, & Sahera Hussein Zain Al-Thalabi. (2022). NONPARAMETRIC ADAPTIVE SMOOTHING WITH PRACTICAL APPLICATION. European Journal of Research Development and Sustainability, 3(9), 69-76. Retrieved from https://scholarzest.com/index.php/ejrds/article/view/2698