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2015 ; 112
(25
): 7629-34
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Statistical learning and selective inference
#MMPMID26100887
Taylor J
; Tibshirani RJ
Proc Natl Acad Sci U S A
2015[Jun]; 112
(25
): 7629-34
PMID26100887
show ga
We describe the problem of "selective inference." This addresses the following
challenge: Having mined a set of data to find potential associations, how do we
properly assess the strength of these associations? The fact that we have
"cherry-picked"--searched for the strongest associations--means that we must set
a higher bar for declaring significant the associations that we see. This
challenge becomes more important in the era of big data and complex statistical
modeling. The cherry tree (dataset) can be very large and the tools for cherry
picking (statistical learning methods) are now very sophisticated. We describe
some recent new developments in selective inference and illustrate their use in
forward stepwise regression, the lasso, and principal components analysis.