Publication:
Two-step semiparametric empirical likelihood inference

dc.affiliation.dptoUC3M. Departamento de Economíaes
dc.contributor.authorEscanciano, Juan Carlos
dc.contributor.authorBravo, Francesco
dc.contributor.authorVan Keilegom, Ingrid
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.contributor.funderComunidad de Madrides
dc.contributor.funderEuropean Commissionen
dc.date.accessioned2022-06-13T13:54:13Z
dc.date.available2022-06-13T13:54:13Z
dc.date.issued2020-02-26
dc.description.abstractIn both parametric and certain nonparametric statistical models, the empirical likelihood ratio satis es a nonparametric version of Wilks' theorem. For many semiparametric models, however, the commonly used two-step (plug-in) empirical likelihood ratio is not asymptotically distribution-free, that is, its asymptotic distribution contains unknown quantities and hence Wilks' theorem breaks down. This article suggests a general approach to restore Wilks' phenomenon in two-step semiparametric empirical likelihood inferences. The main insight consists in using as the moment function in the estimating equation the in uence function of the plug-in sample moment. The proposed method is general; it leads to a chi-squared limiting distribution with known degrees of freedom; it is e cient; it does not require undersmoothing; and it is less sensitive to the rst-step than alternative methods, which is particularly appealing for high-dimensional settings. Several examples and simulation studies illustrate the general applicability of the procedure and its excellent nite sample performance relative to competing methods.en
dc.description.sponsorshipJuan Carlos Escanciano gratefully acknowledges support by the Ministerio Economia y Competitividad (Spain), ECO2017-86675-P & MDM 2014-0431, and by Comunidad de Madrid (Spain), MadEco-CM S2015/HUM-3444. y Ingrid Van Keilegom acknowledges financial support from the European Research Council (2016-2021, Horizon 2020/ERC grant agreement No. 694409).es
dc.identifier.bibliographicCitationBravo, F., Escanciano, J. C., & Van Keilegom, I. (2020). Two-step semiparametric empirical likelihood inference. The Annals of Statistics, 48 (1), pp. 1-26. https://doi.org/10.1214/18-aos1788es
dc.identifier.doihttps://doi.org/10.1214/18-AOS1788
dc.identifier.issn0090-5364
dc.identifier.publicationfirstpage1es
dc.identifier.publicationissue1es
dc.identifier.publicationlastpage26es
dc.identifier.publicationtitleANNALS OF STATISTICSes
dc.identifier.publicationvolume48es
dc.identifier.urihttps://hdl.handle.net/10016/35087
dc.identifier.uxxiAR/0000029574
dc.language.isoenges
dc.publisherInstitute of Mathematical Statisticsen
dc.relation.projectIDGobierno de España. ECO2017-86675-Pes
dc.relation.projectIDGobierno de España. MDM 2014-0431es
dc.relation.projectIDComunidad de Madrid. S2015/HUM-3444es
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/694409
dc.rights© 2020 Institute of Mathematical Statisticsen
dc.rights.accessRightsopen accessen
dc.subject.ecienciaEconomíaes
dc.subject.otherEmpirical likelihooden
dc.subject.otherSemiparametric inferenceen
dc.subject.otherHighdimensional parametersen
dc.subject.otherWilks' phenomenonen
dc.titleTwo-step semiparametric empirical likelihood inferenceen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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