A Markov Chain Genetic Algorithm Approach for Non-Parametric Posterior Distribution Sampling of Regression Parameters
This paper proposes a genetic algorithm-based Markov Chain approach that can be used for non-parametric estimation of regression coefficients and their statistical confidence bounds. The proposed approach can generate samples from an unknown probability density function if a formal functional form o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/17/3/111 |
| _version_ | 1797242330278789120 |
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| author | Parag C. Pendharkar |
| author_facet | Parag C. Pendharkar |
| author_sort | Parag C. Pendharkar |
| collection | DOAJ |
| description | This paper proposes a genetic algorithm-based Markov Chain approach that can be used for non-parametric estimation of regression coefficients and their statistical confidence bounds. The proposed approach can generate samples from an unknown probability density function if a formal functional form of its likelihood is known. The approach is tested in the non-parametric estimation of regression coefficients, where the least-square minimizing function is considered the maximum likelihood of a multivariate distribution. This approach has an advantage over traditional Markov Chain Monte Carlo methods because it is proven to converge and generate unbiased samples computationally efficiently. |
| first_indexed | 2024-04-24T18:37:30Z |
| format | Article |
| id | doaj.art-37ec4d9a327e4d09a4aeb4cb7d4a82e1 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-04-24T18:37:30Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-37ec4d9a327e4d09a4aeb4cb7d4a82e12024-03-27T13:17:24ZengMDPI AGAlgorithms1999-48932024-03-0117311110.3390/a17030111A Markov Chain Genetic Algorithm Approach for Non-Parametric Posterior Distribution Sampling of Regression ParametersParag C. Pendharkar0Information Systems School of Business Administration, Pennsylvania State University at Harrisburg, 777 West Harrisburg Pike, Middletown, PA 17057, USAThis paper proposes a genetic algorithm-based Markov Chain approach that can be used for non-parametric estimation of regression coefficients and their statistical confidence bounds. The proposed approach can generate samples from an unknown probability density function if a formal functional form of its likelihood is known. The approach is tested in the non-parametric estimation of regression coefficients, where the least-square minimizing function is considered the maximum likelihood of a multivariate distribution. This approach has an advantage over traditional Markov Chain Monte Carlo methods because it is proven to converge and generate unbiased samples computationally efficiently.https://www.mdpi.com/1999-4893/17/3/111genetic algorithmsMarkov Chainsregressionnon-parametric estimation algorithm |
| spellingShingle | Parag C. Pendharkar A Markov Chain Genetic Algorithm Approach for Non-Parametric Posterior Distribution Sampling of Regression Parameters Algorithms genetic algorithms Markov Chains regression non-parametric estimation algorithm |
| title | A Markov Chain Genetic Algorithm Approach for Non-Parametric Posterior Distribution Sampling of Regression Parameters |
| title_full | A Markov Chain Genetic Algorithm Approach for Non-Parametric Posterior Distribution Sampling of Regression Parameters |
| title_fullStr | A Markov Chain Genetic Algorithm Approach for Non-Parametric Posterior Distribution Sampling of Regression Parameters |
| title_full_unstemmed | A Markov Chain Genetic Algorithm Approach for Non-Parametric Posterior Distribution Sampling of Regression Parameters |
| title_short | A Markov Chain Genetic Algorithm Approach for Non-Parametric Posterior Distribution Sampling of Regression Parameters |
| title_sort | markov chain genetic algorithm approach for non parametric posterior distribution sampling of regression parameters |
| topic | genetic algorithms Markov Chains regression non-parametric estimation algorithm |
| url | https://www.mdpi.com/1999-4893/17/3/111 |
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