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Journals / Alphanumeric Journal / 2021 / Volume: 9 Issue: 2

Artificial Neural Network approach on Type II Regression Analysis

Berkalp Tunca0000-0002-6501-9963
Sinan Saraçlı(AFYON KOCATEPE ÜNİVERSİTESİ)0000-0003-4662-8031
Pages
—
Publication Date
2021-12-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

With this study, it is foreseen that it will represent an example for researchers who want to work in these fields in the future.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
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Key Findings & Data:In order to measure the performance of this newly created ANN-Bisector technique, it was compared with the OLS-Bisector technique.
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Conclusion & Impact:With this study, it is foreseen that it will represent an example for researchers who want to work in these fields in the future.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Artificial Neural Network · Type II Regression · Measurement Error Models.

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.

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Cite This Article

Tunca, B., Saraçlı, S. (2021). Artificial Neural Network approach on Type II Regression Analysis. Alphanumeric Journal, Volume: 9 Issue: 2. https://doi.org/10.17093/alphanumeric.972138

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