Bayesian lasso principal component regression with an application
DOI:
https://doi.org/10.29304/jqcsm.2023.15.41357Keywords:
LASSO, Principal Component regression, MCMC, Prior distribution .Abstract
The topic of variable selection in constructing statistical models has received wide attention in many applications, including medical, economic, engineering, and various other fields. Since, this method helps researchers save time and effort, by concentrating on crucial variables and eliminating unimportant variables from the statistical models under study. The famous variable selection technique known as the Least absolute Shrinkage and Selection Operator (LASSO) method works by reducing the number of variables with high explanatory power. This study will combine two methods for variable shrinkage, which minimize both simultaneously by removing variables with little effect and reducing the variance and bias of the estimated parameters. Two techniques were used to validate the results of our current study. The first approach uses simulation, where data is produced using predetermined hypotheses and models. The second approach uses data from actual sources .
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Copyright (c) 2024 Mohammed H. AL-Sharoot , Wael abd Al Hussein Kazem, Shatha Awwad Al-Fatlawi
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