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<h2 style="font-size:1.2em;">Seminario de Probabilidad y Estadística</h2>
<h3 style="font-size:1em;">Título: <em>Efficient estimation of Sobol’ indices of any order from a single input/output sample.</em></h3>
<h3 style="font-size:1em;">Expositor: Clémentine Prieur <span style="font-weight:400;">(Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, Grenoble, France)</span></h3>
<div style="font-size:1em!important;"><p><b>Resumen: </b><span>The objective of the present work is to estimate Sobol’ indices at any order in a given-data context, where a unique input/output sample is available. In that view, our approach consists of three main ingredients: one-step inference and nonpositive kernel estimation following [1] together with mirrortype transformations as introduced in [2,3]. We introduce two different estimators that are proved to be asymptotically normal and efficient. In addition, their numerical properties are illustrated on standard examples.</span></p>
<p><span>References:</span></p>
<p><span>[1] K. Doksum and A. Samarov. Nonparametric estimation of global functionals and a measure of the explanatory power of covariates in regression. The Annals of Statistics, 1443–1473, 1995.</span></p>
<p><span>[2] K. Bertin, N. Klutchnikoff, J. R. León, and C. Prieur. Adaptive density estimation on bounded domains under mixing conditions. Electronic Journal of Statistics, 14(1):2198–2237, 2020.</span></p>
<p><span>[3] L. Pujol. Nonparametric estimation of a multivariate density under kullback-leibler loss with ISDE. arXiv preprint arXiv:2205.03199, 2022.</span></p>
<p><span>Joint work with: Sébastien Da Veiga; Univ Rennes, Ensai, CNRS, CREST ; Fabrice Gamboa, Thierry Klein and Agnes Lagnoux; Institut de Mathématiques de Toulouse.</span></p></div>
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