arXiv subjects

Jean-Claude Fort

Publications and source records attributed to Jean-Claude Fort.

9 recordsLinked to original sources

arXiv2020

Global sensitivity analysis and Wasserstein spaces

Sensitivity indices are commonly used to quantity the relative inuence of any specic group of input variables on the output of a computer code. In this paper, we focus both on computer codes the output of which is a cumulative distribution function and on stochastic computer codes. We propose a way to perform a global sensitivity analysis for these kinds of computer codes. In the rst setting, we dene two indices: the rst one is based on Wasserstein Fr{é}chet means while the second one is based on the Hoeding decomposition of the indicators of Wasserstein balls. Further, when dealing with the stochastic computer codes, we dene an ideal version of the stochastic computer code thats ts into the frame of the rst setting. Finally, we deduce a procedure to realize a second level global sensitivity analysis, namely when one is interested in the sensitivity related to the input distributions rather than in the sensitivity related to the inputs themselves. Several numerical studies are proposed as illustrations in the dierent settings.

math.ST↗

arXiv2020

Exact rate of convergence of the mean Wasserstein distance between the empirical and true Gaussian distribution

We study the Wasserstein distance $W_2$ for Gaussian samples. We establish the exact rate of convergence $\sqrt{\log\log n/n}$ of the expected value of the $W_2$ distance between the empirical and true $c.d.f.$'s for the normal distribution. We also show that the rate of weak convergence is unexpectedly $1/\sqrt{n}$ in the case of two correlated Gaussian samples.

math.PR↗

arXiv2019

Weak convergence of empirical Wasserstein type distances

We estimate contrasts $\int_0 ^1 ρ(F^{-1}(u)-G^{-1}(u))du$ between two continuous distributions $F$ and $G$ on $\mathbb R$ such that the set $\{F=G\}$ is a finite union of intervals, possibly empty or $\mathbb{R}$. The non-negative convex cost function $ρ$ is not necessarily symmetric and the sample may come from any joint distribution $H$ on $\mathbb{R}^2$ with marginals $F$ and $G$ having light enough tails with respect to $ρ$. The rates of weak convergence and the limiting distributions are derived in a wide class of situations including the classical Wasserstein distances $W_1$ and $W_2$. The new phenomenon we describe in the case $F=G$ involves the behavior of $ρ$ near $0$, which we assume to be regularly varying with index ranging from $1$ to $2$ and to satisfy a key relation with the behavior of $ρ$ near $\infty$ through the common tails. Rates are then also regularly varying with powers ranging from $1/2$ to $1$ also affecting the limiting distribution, in addition to $H$.

math.PR↗

arXiv2017

A Central Limit Theorem for Wasserstein type distances between two different laws

This article is dedicated to the estimation of Wasserstein distances and Wasserstein costs between two distinct continuous distributions $F$ and $G$ on $\mathbb R$. The estimator is based on the order statistics of (possibly dependent) samples of $F$ resp. $G$. We prove the consistency and the asymptotic normality of our estimators. \begin{it}Keywords:\end{it} Central Limit Theorems- Generelized Wasserstein distances- Empirical processes- Strong approximation- Dependent samples.

math.ST↗

arXiv2015

New Fréchet features for random distributions and associated sensitivity indices

In this article we define new FrÈchet features for random cumulative distribution functions using contrast. These contrasts allow to construct Wasserstein costs and our new features minimize the average costs as the FrÈchet mean minimizes the mean square Wasserstein$_2$ distance. An example of new features is the median, and more generally the quantiles. From these definitions, we are able to define sensitivity indices when the random distribution is the output of a stochastic code. Associated to the FrÈchet mean we extend the Sobol indices, and in general the indices associated to a contrast that we previously proposed.

math.ST↗

arXiv2013

New sensitivity analysis subordinated to a contrast

In a model of the form $Y=h(X_1,\ldots,X_d)$ where the goal is to estimate a parameter of the probability distribution of $Y$, we define new sensitivity indices which quantify the importance of each variable $X_i$ with respect to this parameter of interest. The aim of this paper is to define {\it goal oriented sensitivity indices} and we will show that Sobol indices are sensitivity indices associated to a particular characteristic of the distribution $Y$. We name the framework we present as {\it Goal Oriented Sensitivity Analysis} (GOSA).

stat.ME↗

arXiv2012

Remark on the finite-dimensional character of certain results of functional statistics

This note shows that some assumption on small balls probability, frequently used in the domain of functional statistics, implies that the considered functional space is of finite dimension. To complete this result an example of L2 process is given that does not fulfill this assumption.

math.ST↗

arXiv2007

Theoretical Aspects of the SOM Algorithm

The SOM algorithm is very astonishing. On the one hand, it is very simple to write down and to simulate, its practical properties are clear and easy to observe. But, on the other hand, its theoretical properties still remain without proof in the general case, despite the great efforts of several authors. In this paper, we pass in review the last results and provide some conjectures for the future work.

math.ST↗

arXiv2007

Time Series Forecasting: Obtaining Long Term Trends with Self-Organizing Maps

Kohonen self-organisation maps are a well know classification tool, commonly used in a wide variety of problems, but with limited applications in time series forecasting context. In this paper, we propose a forecasting method specifically designed for multi-dimensional long-term trends prediction, with a double application of the Kohonen algorithm. Practical applications of the method are also presented.

cs.LG↗