| Autor: |
Al-rubaye, Ali Abdulmohsin Abdulraeem, Alhseeni, Ameer Musa Imran |
| Předmět: |
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| Zdroj: |
International Journal of Agricultural & Statistical Sciences; 2021 Suppl, Vol. 17, p1373-1380, 8p, 2 Diagrams, 5 Charts |
| Abstrakt: |
Regression analysis with many predictor variables cannot be considered using the frequentist regression models. In high dimensional data crisis, when the number of predictor variable is greater the sample size (p > n) adopting the penalized Bayesian regression models produces more interpretable models with more prediction accuracy. This is due to the property of variable selection provided by the penalized Bayesian regression models. Group lasso is one of the penalized regression function. In this paper, we proposed a new prior destitution and there fare posterior distributions can be derived based on Bayesian group lasso regression method. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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