@prefix dcat: <http://www.w3.org/ns/dcat#> .
@prefix dct: <http://purl.org/dc/terms/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix vcard: <http://www.w3.org/2006/vcard/ns#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

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    dct:description """
              The aim of this habilitation thesis is to give an overview of my works on high-dimensional statistics and statistical learning, under various sparsity assumptions. In a first part, I will describe the major challenges of high-dimensional statistics in the context of the generic linear regression model. After a brief review of existing results, I will present the theoretical study of aggregated estimators that was done in (Alquier & Lounici 2011). The second part essentially aims at providing extensions of the various theories presented in the first part to the estimation of time series models (Alquier & Doukhan 2011, Alquier & Wintenberger 2013, Alquier & Li 2012, Alquier, Wintenberger & Li 2012). Finally, the third part presents various extensions to nonparametric models, or to specific applications such as quantum statistics (Alquier & Biau 2013, Guedj & Alquier 2013, Alquier, Meziani & Peyré 2013, Alquier, Butucea, Hebiri, Meziani & Morimae 2013, Alquier 2013, Alquier 2008). In each section, we provide explicitely the estimators used and, as much as possible, optimal oracle inequalities satisfied by these estimators.
            """ ;
    dct:identifier "tel-00915505" ;
    dct:issued "2026-05-07T21:56:58.071413"^^xsd:dateTime ;
    dct:language "en" ;
    dct:modified "2026-05-07T21:56:58.071418"^^xsd:dateTime ;
    dct:publisher <https://rec.harvest-normandie.data4citizen.com/organization/cce9db95-46d9-4dc2-84b6-764215d0a002> ;
    dct:title "Contributions to statistical learning in sparse models" ;
    dcat:contactPoint [ a vcard:Organization ;
            vcard:fn "CCSD" ] ;
    dcat:distribution <https://rec.harvest-normandie.data4citizen.com/dataset/oai-hal-tel-00915505v1/resource/fa38d874-3d7d-4da7-8200-7e7d44caf4d5> ;
    dcat:keyword "accreditation-to-supervise-research",
        "aggregated-estimators",
        "dependance-faible",
        "estimateur-lasso",
        "estimateurs-agreges",
        "estimateurs-penalises",
        "high-dimensional-statistics",
        "inegalites-pac-bayesiennes",
        "infoeu-reposemanticsother",
        "lasso-estimator",
        "mathmath-stmathematics-mathstatistics-mathst",
        "methodes-de-monte-carlo",
        "monte-carlo-statistical-methods",
        "pac-bayesian-inequalities",
        "parcimonie",
        "penalized-estimators",
        "quantum-statistics",
        "reduced-rank-regression",
        "regression-matricielle",
        "sparsity",
        "statistical-learning-theory",
        "statistique-en-grande-dimension",
        "statistique-quantique",
        "statthstatistics-statstatistics-theory-statth",
        "theorie-de-lapprentissage-statistique",
        "weak-dependence" ;
    dcat:landingPage <https://theses.hal.science/tel-00915505> .

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    dct:format "HTML" ;
    dct:issued "2026-05-07T21:56:58.102253"^^xsd:dateTime ;
    dct:modified "2026-05-07T21:56:58.038520"^^xsd:dateTime ;
    dct:title "Contributions to statistical learning in sparse models" ;
    dcat:accessURL <https://theses.hal.science/tel-00915505> .

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