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The problem of regression shrinkage and selection for multivariate regression is considered. The goal is to consistently identify those variables relevant for regression. This is done not only for pre...
We study a factor model for the correlation matrix $\Sigma\in\RR^{d\times d}$ of an elliptical copula. The correlations are connected to Kendall's tau and a natural estimation procedure is to plug-in ...
We present an elementary heuristic reasoning based on Arnold's theory of versal deformations in support of a straightforward algorithm for finding a correlation matrix near the given symmetric one.
We introduce a new approach to variable selection, called Predictive Correlation Screening, for predictor design. Predictive Correlation Screening (PCS) implements false positive control on the select...
We study the spatial distribution of clusters associated to the aftershocks of the megathrust Maule earthquakeMW8.8 of 27 February 2010. We used a recent clustering method which hinges on a nonparamet...
We study the spatial distribution of clusters associated to the aftershocks of the megathrust Maule earthquakeMW8.8 of 27 February 2010. We used a recent clustering method which hinges on a nonparamet...
We describe a new optimization scheme for nding high-quality clusterings in planar graphs that uses weighted perfect matching as a subroutine. Our method provides lower-bounds on the energy of the op...
Many nonlinear extensions of the Kalman filter, e.g., the extended and the unscented Kalman filter, reduce the state densities to Gaussian densities. This approximation gives sufficient results in man...
The correlation of the result lists provided by search engines is fundamental and it has deep and multidisciplinary ramifications. Here, we present automatic and unsupervised methods to assess whether...
Statistical emulators of computer simulators have proven to be useful in a variety of applications. The widely adopted model for emulator building, using a Gaussian process model with strictly positiv...
Simulating sample correlation matrices is important in many areas of statistics. Approaches such as generating normal data and finding their sample correlation matrix or generating random uniform $[-1...
Our article considers a regression model with observed factors. The observed factors have a flexible stochastic volatility structure that has separate dynamics for the volatilities and the correlation...
The correlation coefficient between stocks depends on price history and includes information on hierarchical structure in financial markets. It is useful for portfolio selection and estimation of ri...
Two-point correlation functions are used throughout cosmology as a measure for the statistics of random fields. When used in Bayesian parameter estimation, their likelihood function is usually replac...
A trend in compressed sensing (CS) is to exploit struc- ture for improved reconstruction performance. In the basic CS model (i.e. the single measurement vec- tor model), exploiting the clustering s...

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