Zobrazeno 1 - 10
of 92
pro vyhledávání: '"Lacour, Claire"'
This paper studies the estimation of the conditional density f (x, $\times$) of Y i given X i = x, from the observation of an i.i.d. sample (X i , Y i) $\in$ R d , i = 1,. .. , n. We assume that f depends only on r unknown components with typically r
Externí odkaz:
http://arxiv.org/abs/2106.14669
Publikováno v:
Journal of Machine Learning Research 23 (2022) 1-59
Despite the ubiquity of U-statistics in modern Probability and Statistics, their non-asymptotic analysis in a dependent framework may have been overlooked. In a recent work, a new concentration inequality for U-statistics of order two for uniformly e
Externí odkaz:
http://arxiv.org/abs/2106.12796
Autor:
Lacour, Claire, Ngoc, Thanh Mai Pham
We consider X 1 ,. .. , X n a sample of data on the circle S 1 , whose distribution is a twocomponent mixture. Denoting R and Q two rotations on S 1 , the density of the X i 's is assumed to be g(x) = pf (R --1 x) + (1 -- p)f (Q --1 x), where p $\in$
Externí odkaz:
http://arxiv.org/abs/2103.07318
We prove a new concentration inequality for U-statistics of order two for uniformly ergodic Markov chains. Working with bounded and $\pi$-canonical kernels, we show that we can recover the convergence rate of Arcones and Gin{\'e} who proved a concent
Externí odkaz:
http://arxiv.org/abs/2011.11435
We focus on the estimation of the intensity of a Poisson process in the presence of a uniform noise. We propose a kernel-based procedure fully calibrated in theory and practice. We show that our adaptive estimator is optimal from the oracle and minim
Externí odkaz:
http://arxiv.org/abs/2010.04557
Kernel density estimation is a well known method involving a smoothing parameter (the bandwidth) that needs to be tuned by the user. Although this method has been widely used the bandwidth selection remains a challenging issue in terms of balancing a
Externí odkaz:
http://arxiv.org/abs/1902.01075
This article studies the recovery of graphons when they are convolution kernels on compact (symmetric) metric spaces. This case is of particular interest since it covers the situation where the probability of an edge depends only on some unknown nonp
Externí odkaz:
http://arxiv.org/abs/1708.02107
Publikováno v:
Sankhya A, Springer Verlag, 2017, 79 (2), pp.298 - 335
Estimator selection has become a crucial issue in non parametric estimation. Two widely used methods are penalized empirical risk minimization (such as penalized log-likelihood estimation) or pairwise comparison (such as Lepski's method). Our aim in
Externí odkaz:
http://arxiv.org/abs/1607.05091
Autor:
Lacour, Claire, Massart, Pascal
This paper is concerned with adaptive nonparametric estimation using the Goldenshluger-Lepski selection method. This estimator selection method is based on pairwise comparisons between estimators with respect to some loss function. The method also in
Externí odkaz:
http://arxiv.org/abs/1503.00946
We consider stationary hidden Markov models with finite state space and nonparametric modeling of the emission distributions. It has remained unknown until very recently that such models are identifiable. In this paper, we propose a new penalized lea
Externí odkaz:
http://arxiv.org/abs/1501.04787