【摘要】 变量选择和变换选择是线性模型中的两个不同的问题.把这两个过程结合起来同时进行,将是很有意义的.由于近来在计算技术方面的发展,这种同时进行的过程现在是可行的.本文提出了在线性模型中的变数和变换同时进行选择的两个方法.节(?)个方法是(?)个纯粹的同时选择过程.第二个方法适用于具有较多预报因子的数据集,也提出了(?)个对于同时选择的向后删除过程.这两个方法皆以贝叶斯模型选择准则为基础.用(?)个实例来说明这(?)方法.更多还原

【Abstract】 Variable selection and transformation selection are two different problems in the linear model. It is very interesting to combine these two procedures in simultaneous one. Due to recent developments in computing technology, such a simultaneous procedure is now feasible. Two procedures for variable and transformation simultaneous selection are proposed in the linear model. The first procedure is a pure simultaneous selection procedure. The second one. for data sets with mam predictors, a backward elimination procedure for simultaneous selection is also presented. The procedures are based on Bayesian model selection criteria. An example is given to illustrate the methodology.更多还原