Comparing Several Nonlinear Estimators for Regression Function
DOI:
https://doi.org/10.33095/jeas.v18i69.969Keywords:
: دوال الانحدار، المقدر الخطي الموضعي، المقدر شبه المعلمي المدمج، مقدر الشبكة العصبية الاصطناعية، الشبكات العصبية ذات الانتشار العكسي للخطأ, regression function, Local Linear estimator, Combined semi-parametric estimator, Artificial neural network estimator (ANN), Back propagation Neural NetsAbstract
The aim of this paper is to estimate a nonlinear regression function of the Export of the crude oil Saudi (in Million Barrels) as a function of the number of discovered fields.
Through studying the behavior of the data we show that its behavior was not followed a linear pattern or can put it in a known form so far there was no possibility to see a general trend resulting from such exports.
We use different nonlinear estimators to estimate a regression function, Local linear estimator, Semi-parametric as well as an artificial neural network estimator (ANN).
The results proved that the (ANN) estimator is the best nonlinear estimator among the others in estimating the export of crude oil Saudi.
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