A Bayes-Factor-Guided Approach to Post-Double Selection with Bootstrapped Multiple Imputation
自助法多重插补下由贝叶斯因子引导的双重选择后方法
When variable selection methods are applied to bootstrapped and multiply imputed datasets, the set of selected variables typically varies across iterations. Aggregating results via the union rule can lead to overly dense models. We propose a sequential evidence aggregation procedure that models detection outcomes across perturbation iterations as Bernoulli trials and accumulates evidence for variable relevance through a likelihood-ratio process admitting an approximate…