Abstract There are a variety of settings where vague prior information may be available on the importance predictors in high-dimensional regression settings. Examples include ordering variables offered by their empirical variances (which is typically discarded through standardisation), lag when fitting autoregressive models time series settings, or level missingness variables. Whilst such order...

In many applications, it is of interest to assess the relative contribution features (or subsets features) toward goal predicting a response—in other words, gauge variable importance features. Most recent work on assessment has focused describing within confines given prediction algorithm. However, such does not necessarily characterize potential features, and may provide misleading reflection ...