Corpus ID: 237605222

High-dimensional regression with potential prior information on variable importance

@inproceedings{Stokell2021HighdimensionalRW,
  title={High-dimensional regression with potential prior information on variable importance},
  author={Benjamin G. Stokell and Rajen D. Shah},
  year={2021}
}
There are a variety of settings where vague prior information may be available on the importance of predictors in high-dimensional regression settings. Examples include ordering on the variables offered by their empirical variances (which is typically discarded through standardisation), the lag of predictors when fitting autoregressive models in time series settings, or the level of missingness of the variables. Whilst such orderings may not match the true importance of variables, we argue that… Expand

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