The purpose of variable selection is to identify important predictors for response variables. Although there are many varieties of variable selection methods, almost all of them have a problem of not accounting for th...The purpose of variable selection is to identify important predictors for response variables. Although there are many varieties of variable selection methods, almost all of them have a problem of not accounting for the relationship between predictors. Therefore it would well happen that the selected subset of identified predictors leads to hard-to-interpret model consisted of only interaction terms. In design of experiments, the analysis is driven by the effect heredity principle which governs the relationship between an interaction and its corresponding main effects. In this paper, the authors extend the variable selection method the Lasso with effect heredity principle to its Bayesian version. In the example, the authors analyze the data obtained from typical screening design Plackett-Bunnan design and compare the result from the ordinary Bayesian Lasso and proposed method.展开更多
文摘The purpose of variable selection is to identify important predictors for response variables. Although there are many varieties of variable selection methods, almost all of them have a problem of not accounting for the relationship between predictors. Therefore it would well happen that the selected subset of identified predictors leads to hard-to-interpret model consisted of only interaction terms. In design of experiments, the analysis is driven by the effect heredity principle which governs the relationship between an interaction and its corresponding main effects. In this paper, the authors extend the variable selection method the Lasso with effect heredity principle to its Bayesian version. In the example, the authors analyze the data obtained from typical screening design Plackett-Bunnan design and compare the result from the ordinary Bayesian Lasso and proposed method.
