Pareto parameter estimation by merging locally weighted median of multiple neural networks and weighted least squares
The Pareto distribution plays an important role in many data analysis tasks. An important aspect of this distribution is the estimation of its parameters. Several studies use classical methods, Bayes, and the neural network (NN) to evaluate Pareto parameters. Others have attempted to combine classic...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-01-01
|
| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016823011535 |
| _version_ | 1827370662944047104 |
|---|---|
| author | Walid Aydi Mohammed Alatiyyah |
| author_facet | Walid Aydi Mohammed Alatiyyah |
| author_sort | Walid Aydi |
| collection | DOAJ |
| description | The Pareto distribution plays an important role in many data analysis tasks. An important aspect of this distribution is the estimation of its parameters. Several studies use classical methods, Bayes, and the neural network (NN) to evaluate Pareto parameters. Others have attempted to combine classical methods with a single NN-based. However, there isn’t enough research to determine the sensitivity of the single NN to the specifics of the training data due to its stochastic training algorithm in the parameter estimation field. The current research aims to prove the efficiency of the aggregation of weighted multiple NN models and weighted ordinary least-squares regression algorithm to overcome the specifics of the training data and the sensitivity to outliers, respectively. The proposed method enables a locally less accurate model to participate to a lesser extent in the overall aggregation. The proposed method was compared with prevalent methods in the area, including the ordinary least squares, weighted ordinary least squares, maximum likelihood estimation, and the Bayes’ using Monte Carlo simulations. The results verified the superiority of the proposed method in terms of regression error metrics. Moreover, it can be adapted to a variety of distributions. |
| first_indexed | 2024-03-08T10:18:51Z |
| format | Article |
| id | doaj.art-81ed28fdd2fe44248fb22a6e043c1af8 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-03-08T10:18:51Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-81ed28fdd2fe44248fb22a6e043c1af82024-01-28T04:21:12ZengElsevierAlexandria Engineering Journal1110-01682024-01-0187524532Pareto parameter estimation by merging locally weighted median of multiple neural networks and weighted least squaresWalid Aydi0Mohammed Alatiyyah1Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia; Laboratory of Electronics & Information Technologies, Sfax University, Sfax, Tunisia; Corresponding author at: Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia.Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, 11942, Saudi ArabiaThe Pareto distribution plays an important role in many data analysis tasks. An important aspect of this distribution is the estimation of its parameters. Several studies use classical methods, Bayes, and the neural network (NN) to evaluate Pareto parameters. Others have attempted to combine classical methods with a single NN-based. However, there isn’t enough research to determine the sensitivity of the single NN to the specifics of the training data due to its stochastic training algorithm in the parameter estimation field. The current research aims to prove the efficiency of the aggregation of weighted multiple NN models and weighted ordinary least-squares regression algorithm to overcome the specifics of the training data and the sensitivity to outliers, respectively. The proposed method enables a locally less accurate model to participate to a lesser extent in the overall aggregation. The proposed method was compared with prevalent methods in the area, including the ordinary least squares, weighted ordinary least squares, maximum likelihood estimation, and the Bayes’ using Monte Carlo simulations. The results verified the superiority of the proposed method in terms of regression error metrics. Moreover, it can be adapted to a variety of distributions.http://www.sciencedirect.com/science/article/pii/S1110016823011535ParetoMultiple neural network modelWeighted least squaresModel averagingMedian |
| spellingShingle | Walid Aydi Mohammed Alatiyyah Pareto parameter estimation by merging locally weighted median of multiple neural networks and weighted least squares Alexandria Engineering Journal Pareto Multiple neural network model Weighted least squares Model averaging Median |
| title | Pareto parameter estimation by merging locally weighted median of multiple neural networks and weighted least squares |
| title_full | Pareto parameter estimation by merging locally weighted median of multiple neural networks and weighted least squares |
| title_fullStr | Pareto parameter estimation by merging locally weighted median of multiple neural networks and weighted least squares |
| title_full_unstemmed | Pareto parameter estimation by merging locally weighted median of multiple neural networks and weighted least squares |
| title_short | Pareto parameter estimation by merging locally weighted median of multiple neural networks and weighted least squares |
| title_sort | pareto parameter estimation by merging locally weighted median of multiple neural networks and weighted least squares |
| topic | Pareto Multiple neural network model Weighted least squares Model averaging Median |
| url | http://www.sciencedirect.com/science/article/pii/S1110016823011535 |
| work_keys_str_mv | AT walidaydi paretoparameterestimationbymerginglocallyweightedmedianofmultipleneuralnetworksandweightedleastsquares AT mohammedalatiyyah paretoparameterestimationbymerginglocallyweightedmedianofmultipleneuralnetworksandweightedleastsquares |