Corpus ID: 88514377

Fast and General Model Selection using Data Depth and Resampling

@inproceedings{Majumdar2017FastAG,
  title={Fast and General Model Selection using Data Depth and Resampling},
  author={S. Majumdar and Snigdhansu Chatterjee},
  year={2017}
}
We present a technique using data depth functions and resampling to perform best subset variable selection for a wide range of statistical models. We do this by assigning a score, called an $e$-value, to a candidate model, and use a fast bootstrap method to approximate sample versions of these $e$-values. Under general conditions, $e$-values can separate statistical models that adequately explain properties of the data from those that do not. This results in a fast algorithm that fits only a… Expand

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