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20042026
most citedAdaptive Importance Sampling in General Mixture Classes

208 citations

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56 papers · 1 filter

math.ST2026

Concentration of the bootstrap empirical process, with applications to statistical inference

Guillaume Maillard, Adrien Saumard

Considering a general framework of bootstrap with exchangeable weights, we show some concentration inequalities for the supremum of the bootstrap empirical process. On the one hand…

math.ST2026

A Kernel Two-Sample Test Invariant under Group Action with Applications to Functional Data

Madison Giacofci, Anouar Meynaoui, Alex Podgorny

We introduce a kernel-based two-sample test for comparing probability distributions up to group actions. Our construction yields invariant kernels for locally compact -compact g…

math.ST2026

Asymmetric conformal prediction with penalized kernel sum-of-squares

Louis Allain, Sébastien Da Veiga, Brian Staber

Conformal prediction (CP) is a distribution-free method to construct reliable prediction intervals that has gained significant attention in recent years. Despite its success and va…

math.ST2025

Goodness-of-fit testing of the distribution of posterior classification probabilities for validating model-based clustering

Salima El Kolei, Matthieu Marbac

We present the first method for assessing the relevance of a model-based clustering result in a general framework. Standard validation criteria, like the adjusted Rand index, rely…

math.ST2025

Adaptive Algorithms for Infinitely Many-Armed Bandits: A Unified Framework

Emmanuel Pilliat

We consider a bandit problem where the buget is smaller than the number of arms, which may be infinite. In this regime, the usual objective in the literature is to minimize simple…

math.ST2025

On the pointwise and sup-norm errors for local regression estimators

Jérémy Bettinger, François Portier, Adrien Saumard

In this paper, we analyze the behavior of various non-parametric local regression estimators, i.e. estimators that are based on local averaging, for estimating a Lipschitz regressi…