EigenPrism
swMATH ID:  21947 
Software Authors:  Janson, Lucas; Barber, Rina Foygel; Candès, Emmanuel 
Description:  EigenPrism: inference for high dimensional signaltonoise ratios. Consider the following three important problems in statistical inference: constructing confidence intervals for the error of a high dimensional (p>n) regression estimator, the linear regression noise level and the genetic signaltonoise ratio of a continuousvalued trait (related to the heritability). All three problems turn out to be closely related to the littlestudied problem of performing inference on the l 2 norm of the signal in high dimensional linear regression. We derive a novel procedure for this, which is asymptotically correct when the covariates are multivariate Gaussian and produces valid confidence intervals in finite samples as well. The procedure, called EigenPrism, is computationally fast and makes no assumptions on coefficient sparsity or knowledge of the noise level. We investigate the width of the EigenPrism confidence intervals, including a comparison with a Bayesian setting in which our interval is just 5 
Homepage:  https://arxiv.org/pdf/1505.02097.pdf 
Keywords:  eigenprism; heritability; regression error; signaltonoise ratio; variance estimation 
Related Software:  covTest; CorrT; selectiveInference; hdi; selectiveinference; Eigenstrat; GitHub; MTG2; KBAL; sbw; ElemStatLearn; FASTCLIME; SetTest; multcomp; Bioconductor; R; CVX 
Cited in:  16 Publications 
Standard Articles
1 Publication describing the Software, including 1 Publication in zbMATH  Year 

EigenPrism: inference for high dimensional signaltonoise ratios. Zbl 1373.62355 Janson, Lucas; Barber, Rina Foygel; Candès, Emmanuel 
2017

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Cited by 30 Authors
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Cited in 8 Serials
Cited in 4 Fields
16  Statistics (62XX) 
1  Probability theory and stochastic processes (60XX) 
1  Computer science (68XX) 
1  Information and communication theory, circuits (94XX) 