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We study the fundamental limits of detecting the presence of an additive rank-one perturbation, or spike, to a Wigner matrix. When the spike comes from a prior that is i.i.d. across coordinates, we prove that the log-likelihood ratio of the spiked model against the non-spiked one is asymptotically normal below a certain reconstruction threshold which is not necessarily of a spectral nature, and that it is degenerate above. This establishes the maximal region of contiguity between the planted and null models. It is known that this threshold also marks a phase transition for estimating the spike: the latter task is possible above the threshold and impossible below. Therefore, both estimation and detection undergo the same transition in this random matrix model. We also provide further information about the performance of the optimal test. Our proofs are based on Gaussian interpolation methods and a rigorous incarnation of the cavity method, as devised by Guerra and Talagrand in their study of the Sherrington--Kirkpatrick spin-glass model.
We consider the problem of estimating a large rank-one tensor ${boldsymbol u}^{otimes k}in({mathbb R}^{n})^{otimes k}$, $kge 3$ in Gaussian noise. Earlier work characterized a critical signal-to-noise ratio $lambda_{Bayes}= O(1)$ above which an ideal
In this paper, we draw attention to a problem that is often overlooked or ignored by companies practicing hypothesis testing (A/B testing) in online environments. We show that conducting experiments on limited inventory that is shared between variant
Let $G$ be a non--linear function of a Gaussian process ${X_t}_{tinmathbb{Z}}$ with long--range dependence. The resulting process ${G(X_t)}_{tinmathbb{Z}}$ is not Gaussian when $G$ is not linear. We consider random wavelet coefficients associated wit
We propose a Bayesian methodology for estimating spiked covariance matrices with jointly sparse structure in high dimensions. The spiked covariance matrix is reparametrized in terms of the latent factor model, where the loading matrix is equipped wit
We mainly study the M-estimation method for the high-dimensional linear regression model, and discuss the properties of M-estimator when the penalty term is the local linear approximation. In fact, M-estimation method is a framework, which covers the