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This manuscript provides optimization guarantees, generalization bounds, and statistical consistency results for AdaBoost variants which replace the exponential loss with the logistic and similar loss...
Penalized regression models are popularly used in high-dimensional data analysis to conduct variable selection and model fitting simultaneously. Whereas success has been widely reported in literature,...
We study a family of Markov processes on $\mathcal{P}^{(k)}$, the space of partitions of the natural numbers with at most $k$ blocks. The process can be constructed from a Poisson point process on $\m...
In array processing, a common problem is to estimate the angles of arrival of $K$ deterministic sources impinging on an array of $M$ antennas, from $N$ observations of the source signal, corrupted by ...
A maximum likelihood based model selection of discrete Bayesian networks is considered. The model selection is performed through scoring function S, which, for a given network G and n-sample Dn, is ...
We address the issue of variable selection in the regression model with very high ambient dimension, i.e., when the number of covariates is very large. The main focus is on the situation where the num...
We address the issue of variable selection in the regression model with very high ambient dimension, i.e., when the number of covariates is very large. The main focus is on the situation where the nu...
Consider an i.i.d. sequence of random variables whose distribution f lies in one of a nested family of models (Mq)q2N,Mq  Mq+1. The smallest index q such that Mq contains f is called the model orde...
We prove the statistical consistency of kernel Partial Least Squares Regression applied to a bounded regression learning problem on a re- producing kernel Hilbert space. Partial Least Squares stands...
We propose a new test for multivariate normality based on the empirical characteristic function. We show that the test is afine invariant and consistent against every non-normal alternative. The te...
For multivariate normal models and some exponential family models a multiple testing stepwise method is offered that is both admissible and consistent. The method is readily adaptable to selecting var...
The so-called Hodrick-Prescott filter was first introduced in actuarial science to estimate trends from claims data and now is widely used in economics and finance to estimate and predict e.g. busines...
Fisher-consistent loss functions play a fundamental role in the construction of successful binary margin-based classifiers. In this paper we establish the Fisher-consistency condition for multicateg...
Estimating covariance matrices is a problem of fundamental importance in multivariate statistics. In practice it is increasingly frequent to work with data matrices X of dimension n×p, where p and n...
We consider the least-square linear regression problem with regularization by the ℓ1-norm, a problem usually referred to as the Lasso. In this paper, we first present a detailed asymptotic ana...

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