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The estimation problem in a high regression model with structured sparsity is investigated.An algorithm using a two steps block thresholding procedure called GR-LOL is provided.Convergence rates are p...
The machine learning community has recently devoted much attention to the problem of inferring causal relationships from statistical data. Most of this work has focused on uncovering connections among...
A general stochastic model is developed for the total interference in wideband systems, denoted as the PNSC(α) Interference Model. It allows one to obtain, analytic representations in situations where...
Statistical inference on the mean of a Poisson distribution is a fundamentally important problem with modern applications in, e.g., particle physics. The dis-creteness of the Poisson distribution make...
The PC algorithm uses conditional independence tests for model selection in graphical modeling with acyclic directed graphs. In Gaussian mod-els, tests of conditional independence are typically based ...
In this paper we study the limiting distributions of the least-squares estimators for the non-stationary first-order threshold autoregressive (TAR(1)) model. It is proved that the limiting behaviors o...
Purely data driven approaches for machine learning present difficulties when data is scarce relative to the complexity of the model or when the model is forced to extrapolate.
In many conventional scientific investigations with high or ultra-high dimensional feature spaces, the relevant features, though sparse, are large in number compared with classical statistical problem...
Machine learning approaches to multi-label document classification have (to date) largely relied on discriminative modeling techniques such as support vector machines. A drawback of these approaches i...
Machine learning approaches to multi-label document classification have (to date) largely relied on discriminative modeling techniques such as support vector machines. A drawback of these approaches i...
A Monte Carlo EM algorithm is considered for the maximum likelihood estimation of multivariate probit models.
In this paper we propose a new wider class of hypergeometric heavy tailed priors that are given as the convolution of a Student-t density for the location parameter and a Scaled Beta2 prior for the va...
We consider the problem of high-dimensional Ising (graphical) model selection. We propose a simple algorithm for structure estimation based on the thresholding of the empirical conditional mutual info...
A predictive Bayesian model selection approach is presented to discriminate coupled models used to predict an unobserved quantity of interest (QoI).
This paper studies the change point problem for a general parametric, univariate or multivariate family of distributions.

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