Regularization Methods for High-Dimensional Inference

David A. Shaw
High dimensionality is a common problem in statistical inference, and is becoming more prevalent in modern data analysis settings. While often data of interest may have a large -- often unmanageable -- dimension, modifications to various well-known techniques can be made to improve performance and aid interpretation. We typically assume that although predictors lie in a high-dimensional ambient space, they have a lower-dimensional structure that can be exploited through either prior knowledge or estimation. In...
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