FILLATRE , Lionel

International audience

Cet article s'intéresse au calcul d'un test minimax de niveau contraint entre plusieurs hypothèses impliquant des observations discrètes et une fonction de perte arbitraire. Le test minimax de niveau contraint minimise le risque de classification maximum et il garantit simultanément que la probabilité de rejeter l'hypothèse nulle, appelé le...

This paper deals with the detection of hidden bits in the Least Significant Bit (LSB) plane of a natural image. The mean level and the covariance matrix of the image, considered as a quantized Gaussian random matrix, are unknown. An adaptive statistical test is designed such that its probability distribution is always independent of the unknown...

This paper deals with the detection of hidden bits in the Least Significant Bit (LSB) plane of a natural image. The mean level and the covariance matrix of the image, considered as a quantized Gaussian random matrix, are unknown. An adaptive statistical test is designed such that its probability distribution is always independent of the unknown...

This paper develops a multihypothesis testing framework for calculating numerically the optimal minimax test with discrete observations and an arbitrary loss function. Discrete observations are common in data processing and make tractable the calculation of the minimax test. Each hypothesis is both associated to a parameter defining the...

International audience

International audience

International audience

International audience

International audience

This paper studies the problem of classifying some Gaussian samples into one of two parametric probabilistic models, also called sources, when the parameter and the a priori probability of each source are unknown. Each source is governed by an univariate normal distribution whose mean is unknown. A training sequence is available for each source...

This paper addresses the problem of multiple hypothesis testing (detection and isolation of mean vectors) in the case of Gaussian linear model with nuisance parameters. An invariant constrained asymptotically uniformly minimax test is proposed to solve this problem. The invariance of the test with respect to the nuisance parameters is obtained...