Diel, Roland Le Corff, Sylvain Lerasle, Matthieu

In this paper, we estimate the distribution of hidden nodes weights in large random graphs from the observation of very few edges weights. In this very sparse setting, the first non-asymptotic risk bounds for maximum likelihood estimators (MLE) are established. The proof relies on the construction of a graphical model encoding conditional dependenc...

Azaïs, Jean-Marc Bachoc, François Klein, Thierry Lagnoux, Agnès Nguyen, Thi Mong Ngoc

We consider the semi-parametric estimation of a scale parameter of a one-dimensional Gaussian process with known smoothness. We suggest an estimator based on quadratic variations and on the moment method. We provide asymptotic approximations of the mean and variance of this estimator, together with asymptotic normality results, for a large class of...

Daouia, Abdelaati Girard, Stephane Stupfler, Gilles

Expectiles define a least squares analogue of quantiles. They are determined by tail expectations rather than tail probabilities. For this reason and many other theoretical and practical merits, expec-tiles have recently received a lot of attention, especially in actuarial and financial risk management. Their estimation, however, typically requires...

Castellan, Gwenaëlle Cousien, Anthony Tran, Viet Chi

The global sensitivity analysis is a set of methods aiming at quantifying the contribution of an uncertain input parameter of the model (or combination of parameters) on the variability of the response. We consider here the estimation of the Sobol indices of order 1 which are commonly-used indicators based on a decomposition of the output's varianc...

Albert, Clément Dutfoy, Anne Girard, Stéphane

We investigate the asymptotic behavior of the (relative) extrapolation error associated with some estimators of extreme quantiles based on extreme-value theory. It is shown that the extrapolation error can be interpreted as the remainder of a first order Taylor expansion. Necessary and sufficient conditions are then provided such that this error te...

Albert, Clément Dutfoy, Anne Gardes, Laurent Girard, Stéphane

We propose a new estimator for extreme quantiles under the log-generalized Weibull-tail model, introduced by Cees de Valk. This model relies on a new regular variation condition which, in some situations, permits to extrapolate further into the tails than the classical assumption in extreme-value theory. The asymptotic normality of the estimator is...

Huet, Sylvie Taupin, Marie-Luce

We propose to estimate a metamodel and the sensitivity indices of a complex model m in the Gaussian regression framework. Our approach combines methods for sensitivity analysis of complex models and statistical tools for sparse non-parametric estimation in multivariate Gaussian regression model. It rests on the construction of a metamodel for aprox...

Roche, Angelina

In more and more applications, a quantity of interest may depend on several covariates, with at least one of them infinite-dimensional (e.g. a curve). To select relevant covariate in this context, we propose an adaptation of the Lasso method. The criterion is based on classical Lasso inference under group sparsity (Yuan and Lin, 2006; Lounici et al...

Chatelain, Jean-Bernard Ralf, Kirsten

A number of macroeconomic theories, very popular in the 1980s, seem to have completely disappeared and been replaced by the dynamic stochastic general equilibrium (DSGE) approach. We will argue that this replacement is due to a tacit agreement on a number of assumptions, previously seen as mutually exclusive, and not due to a settlement by 'nature'...

Dion, Charlotte Hermann, Simone Samson, Adeline

Stochastic differential equations (SDEs) are useful to model continuous stochastic processes. When (independent) repeated temporal data are available, variability between the trajectories can be modeled by introducing random effects in the drift of the SDEs. These models are useful to analyse neuronal data, crack length data, pharmacokinetics, fina...