Statistical significance in high-dimensional linear models

Details Statistical significance in high-dimensional linear models Statistical significance in high-dimensional linear models

2012-02-07

arxiv.org/abs/1202.1377v1

Statistics

Methodology

32 pages, 3 figures

Scientific paper

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. We prove strong error control for our p-values and provide sufficient conditions for detection: for the former, we do not make any assumption on the size of the true underlying regression coefficients. We demonstrate the method in simulated examples and a real data application.

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