Tests alternative to higher criticism for high-dimensional means under sparsity and column-wise dependence

We consider two alternative tests to the Higher Criticism test of Donoho and Jin [Ann. Statist. 32 (2004) 962-994] for high-dimensional means under the sparsity of the nonzero means for sub-Gaussian distributed data with unknown column-wise dependence. The two alternative test statistics are constructed by first thresholding $L_1$ and $L_2$ statistics based on the sample means, respectively, followed by maximizing over a range of thresholding levels to make the tests adaptive to the unknown signal strength and sparsity. The two alternative tests can attain the same detection boundary of the Higher Criticism test in [Ann. Statist. 32 (2004) 962-994] which was established for uncorrelated Gaussian data. It is demonstrated that the maximal $L_2$-thresholding test is at least as powerful as the maximal $L_1$-thresholding test, and both the maximal $L_2$ and $L_1$-thresholding tests are at least as powerful as the Higher Criticism test.Comment: Published in at http://dx.doi.org/10.1214/13-AOS1168 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Multidimensional two-component Gaussian mixtures detection

International audienceLet $(X_1,\ldots,X_n)$ be a $d$-dimensional i.i.d sample from a distribution with density $f$. The problem of detection of a two-component mixture is considered. Our aim is to decide whether $f$ is the density of a standard Gaussian random $d$-vector ($f=\phi_d$) against $f$ is a two-component mixture: $f=(1-\varepsilon)\phi_d +\varepsilon \phi_d (.-\mu)$ where $(\varepsilon,\mu)$ are unknown parameters. Optimal separation conditions on $\varepsilon, \mu, n$ and the dimension $d$ are established, allowing to separate both hypotheses with prescribed errors. Several testing procedures are proposed and two alternative subsets are considered