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Linear classi cation analysis is a fundamental tool for science and technology. In important application elds today { genomics and proteomics are examples { one automatically obtains very high-dimen...
Control of the False Discovery Rate (FDR) is a recent innovation in multiple hypothesis testing, allowing the user to limit the fraction of rejected null hypotheses which correspond to false rejecti...
We consider the sparse inverse covariance regularization problem or graphical lasso with regularization parameter λ. Suppose the sample covariance graph formed by thresholding the entries of the sampl...
Given data from a spherical Gaussian distribution with unknown mean vector θ, estimates of quadratic functionals are constructed by thresholding. Mean squared error bounds are derived via a comparis...
Density estimation is a commonly used test case for non-parametric estimation methods. We explore the asymptotic properties of estimators based on thresholding of empirical wavelet coecients. Minim...
We study the problem of estimating  from data Y  N(; 2 ) under squared-error loss. We de ne three new scalar minimax problems in which the risk is weighted by the size of . Simple thresholding...
This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood as the convex relaxat...
In an increasing number of applications, it is of interest to recover an approximately low-rank data matrix from noisy observations. This paper develops an unbiased risk estimateholding in a Gaussian...
Abstract: This paper deals with the study of dependencies between two given events modeled by point processes. In particular, we focus on the context of DNA to detect favored or avoided distances betw...

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