"Multiple Testing and Adaptive Estimation via the Sorted L-One Norm" (Next Week at the Statistics Seminar)

Attention conservation notice: Only of interest if you (1) want to do high-dimensional regressions without claiming lots of discoveries which turn out to be false, and (2) will be in Pittsburgh on Monday.

"But Cosma", I hear you asking, "how can you be five talks into the spring seminar series without having had a single talk about false discovery rate control? Is the CMU department feeling quite itself?" I think you for your concern, and hope this will set your mind at ease:

Weijie Su, "Multiple Testing and Adaptive Estimation via the Sorted L-One Norm"

Abstract: In many real-world statistical problems, we observe a large number of potentially explanatory variables of which a majority may be irrelevant. For this type of problem, controlling the false discovery rate (FDR) guarantees that most of the discoveries are truly explanatory and thus replicable. In this talk, we propose a new method named SLOPE to control the FDR in sparse high-dimensional linear regression. This computationally efficient procedure works by regularizing the fitted coefficients according to their ranks: the higher the rank, the larger the penalty. This is analogous to the Benjamini-Hochberg procedure, which compares more significant p-values with more stringent thresholds. Whenever the columns of the design matrix are not strongly correlated, we show empirically that SLOPE obtains FDR control at a reasonable level while offering substantial power.

Although SLOPE is developed from a multiple testing viewpoint, we show the surprising result that it achieves optimal squared errors under Gaussian random designs over a wide range of sparsity classes. An appealing feature is that SLOPE does not require any knowledge of the degree of sparsity. This adaptivitiy to unknown sparsity has to do with the FDR control, which strikes the right balance between bias and variance. The proof of this result presents several elements not found in the high-dimensional statistics literature.

Time and place: 4--5 pm on Monday, 22 February 2016, in Scaife Hall 125.

As always, the talk is free and open to the public.

Enigmas of Chance