5 citations · 15 across the 17 of their papers we have counts for
18 papers
Can we have it all? Non-asymptotically valid and asymptotically exact confidence intervals for expectations and linear regressions
Alexis Derumigny, Lucas Girard, Yannick Guyonvarch
We contribute to bridging the gap between large- and finite-sample inference by studying confidence sets (CSs) that are both non-asymptotically valid and asymptotically exact unifo…
Lower bounds for the trade-off between bias and mean absolute deviation
Alexis Derumigny, Johannes Schmidt-Hieber
In nonparametric statistics, rate-optimal estimators typically balance bias and stochastic error. The recent work on overparametrization raises the question whether rate-optimal es…
Codivergences and information matrices
Alexis Derumigny, Johannes Schmidt-Hieber
We propose a new concept of codivergence, which quantifies the similarity between two probability measures relative to a reference probability measure . In the neig…
Fast estimation of Kendall's Tau and conditional Kendall's Tau matrices under structural assumptions
Rutger van der Spek, Alexis Derumigny
Kendall's tau and conditional Kendall's tau matrices are multivariate (conditional) dependence measures between the components of a random vector. For large dimensions, available e…
Identifiability and estimation of meta-elliptical copula generators
Alexis Derumigny, Jean-David Fermanian
Meta-elliptical copulas are often proposed to model dependence between the components of a random vector. They are specified by a correlation matrix and a map , called density g…
Robust-to-outliers square-root LASSO, simultaneous inference with a MOM approach
G. Finocchio, A. Derumigny, K. Proksch
We consider the least-squares regression problem with unknown noise variance, where the observed data points are allowed to be corrupted by outliers. Building on the median-of-mean…
Explicit non-asymptotic bounds for the distance to the first-order Edgeworth expansion
Alexis Derumigny, Lucas Girard, Yannick Guyonvarch
In this article, we obtain explicit bounds on the uniform distance between the cumulative distribution function of a standardized sum of independent centered random varia…
Estimation of copulas via Maximum Mean Discrepancy
Pierre Alquier, Badr-Eddine Chérief-Abdellatif, Alexis Derumigny +1
This paper deals with robust inference for parametric copula models. Estimation using Canonical Maximum Likelihood might be unstable, especially in the presence of outliers. We pro…
Testing for equality between conditional copulas given discretized conditioning events
Alexis Derumigny, Jean-David Fermanian, Aleksey Min
Several procedures have been recently proposed to test the simplifying assumption for conditional copulas. Instead of considering pointwise conditioning events, we study the consta…
Conditional empirical copula processes and generalized dependence measures
Alexis Derumigny, Jean-David Fermanian
We study the weak convergence of conditional empirical copula processes, when the conditioning event has a nonzero probability. The validity of several bootstrap schemes is stated,…
On lower bounds for the bias-variance trade-off
Alexis Derumigny, Johannes Schmidt-Hieber
It is a common phenomenon that for high-dimensional and nonparametric statistical models, rate-optimal estimators balance squared bias and variance. Although this balancing is wide…
On the construction of confidence intervals for ratios of expectations
Alexis Derumigny, Lucas Girard, Yannick Guyonvarch
In econometrics, many parameters of interest can be written as ratios of expectations. The main approach to construct confidence intervals for such parameters is the delta method.…
Estimation of a regular conditional functional by conditional U-statistics regression
Alexis Derumigny
U-statistics constitute a large class of estimators, generalizing the empirical mean of a random variable to sums over every -tuple of distinct observations of . They may…
On kernel-based estimation of conditional Kendall's tau: finite-distance bounds and asymptotic behavior
Alexis Derumigny, Jean-David Fermanian
We study nonparametric estimators of conditional Kendall's tau, a measure of concordance between two random variables given some covariates. We prove non-asymptotic bounds with exp…
A classification point-of-view about conditional Kendall's tau
Alexis Derumigny, Jean-David Fermanian
We show how the problem of estimating conditional Kendall's tau can be rewritten as a classification task. Conditional Kendall's tau is a conditional dependence parameter that is a…
About Kendall's regression
Alexis Derumigny, Jean-David Fermanian
Conditional Kendall's tau is a measure of dependence between two random variables, conditionally on some covariates. We assume a regression-type relationship between conditional Ke…
Improved bounds for Square-Root Lasso and Square-Root Slope
Alexis Derumigny
Extending the results of Bellec, Lecué and Tsybakov to the setting of sparse high-dimensional linear regression with unknown variance, we show that two estimators, the Square-Root…
About tests of the "simplifying" assumption for conditional copulas
Alexis Derumigny, Jean-David Fermanian
We discuss the so-called "simplifying assumption" of conditional copulas in a general framework. We introduce several tests of the latter assumption for non- and semiparametric cop…