Conjecture on the global-null correlation maximum bound

Let fn(x)f_n(x) be the density of nSij\sqrt{n}|S_{ij}|, let Fˉn(x)=xnfn(x)\bar{F}_n(x)=\int_x^{\sqrt{n}}f_n(x), and let Gn,p(x)G_{n,p}(x) be the cumulative distribution function of the maximum of an independent p×pp\times p correlation matrix based on nn observed pp-vectors, scaled by n\sqrt{n}. Global-null correlation maximum conjecture. For fixed kk,

0(42k+2)logpGn,p(x)Fˉnk1(x)fn(x)dx=o(1p2k).\int_0^{\sqrt{\left(4-\frac{2}{k+2}\right)\log p}}G_{n,p}(x)\bar{F}_n^{k-1}(x)f_n(x)\,dx=o\left(\frac{1}{p^{2k}}\right).

This conjecture is the technical bound needed to apply the correlation-limit approximation when controlling the probability that kk independent correlations exceed the maximum correlation of a large correlation matrix. The paper states that it is needed to prove the asymptotic exponential law TkdExp(1/k)T_k\stackrel{d}{\rightarrow}\operatorname{Exp}(1/k) under the global null, but reports that it has not yet been proved.

Sources & referencesView supporting material

Primary source

Max Grazier G'Sell, Jonathan Taylor and Robert Tibshirani, “Adaptive testing for the graphical lasso”, arXiv:1307.4765 (2013).

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