Sylvie Huet (2006)
ESAIM: Probability and Statistics
Similarity:
We propose a method based on a penalised likelihood criterion, for estimating the number on non-zero components of the mean of a Gaussian vector. Following the work of Birgé and Massart in Gaussian model selection, we choose the penalty function such that the resulting estimator minimises the Kullback risk.
Pierre Alquier (2008)
ESAIM: Probability and Statistics
Similarity:
We propose a feature selection method for density estimation with quadratic loss. This method relies on the study of unidimensional approximation models and on the definition of confidence regions for the density thanks to these models. It is quite general and includes cases of interest like detection of relevant wavelets coefficients or selection of support vectors in SVM. In the general case, we prove that every selected feature actually improves the performance of the estimator....
Asunción Rubio, Jan Amos Vísek (1997)
Qüestiió
Similarity:
An estimator of the contamination level of data is proposed in the framework of linear models and its asymptotic behavior is investigated. A numerical study illustrates its finite sample performance under an alternative.
Lucien Birgé, Yves Rozenholc (2006)
ESAIM: Probability and Statistics
Similarity:
Given an -sample from some unknown density on , it is easy to construct an histogram of the data based on some given partition of , but not so much is known about an optimal choice of the partition, especially when the data set is not large, even if one restricts to partitions into intervals of equal length. Existing methods are either rules of thumbs or based on asymptotic considerations and often involve some smoothness properties of . Our purpose in this paper is to give an automatic,...