Artificial Neural Network approach on Type II Regression Analysis

Tunca, Berkalp; Saraçlı, Sinan

doi:http://dx.doi.org/10.17093/alphanumeric.972138

Artificial Neural Network approach on Type II Regression Analysis

bib

Berkalp Tunca

Sinan Saraçlı, Ph.D.

Abstract

In this study, the Artificial Neural Network (ANN) approach was applied to the OLS-Bisector technique, which is one of the Type II Regression techniques, through this study. In order to measure the performance of this newly created ANN-Bisector technique, it was compared with the OLS-Bisector technique. First of all, literature information on ANN and OLS-Bisector Regression techniques is given, and the features of two techniques are mentioned. In line with this information, a comparison was made between OLS based bisector technique and ANN based bisector techniques. In order to compare these two techniques, they were modeled in different distributions and in different sample sizes. In order to compare the performances of these models, the "Mean Absolute Percent Error" (MAPE) criterion was used. As a result of the study, it was seen that the ANN based bisector technique gave better results with lower error than the OLS based bisector technique. With this study, it is foreseen that it will represent an example for researchers who want to work in these fields in the future.

Keywords: Artificial Neural Networks, Measurement Error Models, Type II Regression

Jel Classification: C46

Suggested citation

Tunca, B. & Saraçlı, S. (2021). Artificial Neural Network approach on Type II Regression Analysis. Alphanumeric Journal, 9(2), 247-258. http://dx.doi.org/10.17093/alphanumeric.972138

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2021.09.02.STAT.02

alphanumeric journal

Volume 9, Issue 2, 2021

Pages 247-258

Received: July 15, 2021

Accepted: Oct. 27, 2021

Published: Dec. 31, 2021

Full Text [654.8 KB]

2021 Tunca, B., Saraçlı, S.

This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence, which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

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