Zobrazeno 1 - 10
of 74
pro vyhledávání: '"Marteau Clément"'
We investigate the theoretical performances of the Partial Least Square (PLS) algorithm in a high dimensional context. We provide upper bounds on the risk in prediction for the statistical linear model when considering the PLS estimator. Our bounds a
Externí odkaz:
http://arxiv.org/abs/2410.10237
Autor:
Marteau Clément, Zani Marguerite
Publikováno v:
ESAIM: Proceedings and Surveys, Vol 74, Pp 1-1 (2023)
Externí odkaz:
https://doaj.org/article/2fda49f09fab47aaa89c7787ac20c1b5
This paper presents a novel algorithm that leverages Stochastic Gradient Descent strategies in conjunction with Random Features to augment the scalability of Conic Particle Gradient Descent (CPGD) specifically tailored for solving sparse optimisation
Externí odkaz:
http://arxiv.org/abs/2312.05993
This paper investigates some theoretical properties of the Partial Least Square (PLS) method. We focus our attention on the single component case, that provides a useful framework to understand the underlying mechanism. We provide a non-asymptotic up
Externí odkaz:
http://arxiv.org/abs/2310.10115
Relating a set of variables X to a response y is crucial in chemometrics. A quantitative prediction objective can be enriched by qualitative data interpretation, for instance by locating the most influential features. When high-dimensional problems a
Externí odkaz:
http://arxiv.org/abs/2301.07206
This paper investigates the statistical estimation of a discrete mixing measure $\mu$0 involved in a kernel mixture model. Using some recent advances in l1-regularization over the space of measures, we introduce a "data fitting and regularization" co
Externí odkaz:
http://arxiv.org/abs/1907.10592
This paper extends the successful maxiset paradigm from function estimation to signal detection in inverse problems. In this context, the maxisets do not have the same shape compared to the classical estimation framework. Nevertheless, we introduce a
Externí odkaz:
http://arxiv.org/abs/1803.05875
We consider the binary supervised classification problem with the Gaussian functional model introduced in [7]. Taking advantage of the Gaussian structure, we design a natural plug-in classifier and derive a family of upper bounds on its worst-case ex
Externí odkaz:
http://arxiv.org/abs/1801.03345
Autor:
Marteau, Clement, Sapatinas, Theofanis
Publikováno v:
Mathematical Methods of Statistics, Vol. 26, 282-298 (2017)
We consider minimax signal detection in the sequence model. Working with certain ellipsoids in the space of square-summable sequences of real numbers, with a ball of positive radius removed, we obtain upper and lower bounds for the minimax separation
Externí odkaz:
http://arxiv.org/abs/1611.09008
In this paper, we consider a parametric density contamination model. We work with a sample of i.i.d. data with a common density, $f^\star =(1-\lambda^\star) \phi + \lambda^\star \phi(.-\mu^\star)$, where the shape $\phi$ is assumed to be known. We es
Externí odkaz:
http://arxiv.org/abs/1604.00306