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Evaluating Predictive Accuracy of Regression Models with First-Order Autoregressive Disturbances: A Comparative Approach Using Artificial Neural Networks and Classical Estimators.

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Title: Evaluating Predictive Accuracy of Regression Models with First-Order Autoregressive Disturbances: A Comparative Approach Using Artificial Neural Networks and Classical Estimators.
Authors: Rauf, Rauf I.1 (AUTHOR) rauf.ibrahim@uniabuja.edu.ng, Alrasheedi, Masad A.2 (AUTHOR) mrshedi@taibahu.edu.sa, Sadiq, Rasheedah3 (AUTHOR) rosadiq@nigerianstat.gov.ng, Aldawsari, Abdulrahman M. A.4 (AUTHOR) abd.aldawsari@psau.edu.sa
Source: Mathematics (2227-7390). Dec2024, Vol. 12 Issue 24, p3966. 23p.
Database: Academic Search Ultimate
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  Data: Evaluating Predictive Accuracy of Regression Models with First-Order Autoregressive Disturbances: A Comparative Approach Using Artificial Neural Networks and Classical Estimators.
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  Data: <searchLink fieldCode="JN" term="%22Mathematics+%282227-7390%29%22">Mathematics (2227-7390)</searchLink>. Dec2024, Vol. 12 Issue 24, p3966. 23p.
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        Value: 10.3390/math12243966
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        Text: English
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              Text: Dec2024
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              Y: 2024
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