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2012

Statistical significance in high-dimensional linear models

Bühlmann, Peter

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We propose a method for constructing p-values for general hypotheses in a high-dimensional linear model.

The hypotheses can be local for testing a single regression parameter or they may be more global involving several up to all parameters.
Furthermore, when considering many hypotheses, we show how to adjust for multiple testing taking dependence among the p-values into account.
Our technique is based on Ridge estimation with an additional correction term due to a substantial projection bias in high dimensions.

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