Subsampling inference in cube root asymptotics with an application to manski's maximum score estimator

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dc.contributor.author Delgado, Miguel A.
dc.contributor.author Rodríguez Poo, Juan M.
dc.contributor.author Wolf, Michael
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned 2011-01-26T17:25:11Z
dc.date.available 2011-01-26T17:25:11Z
dc.date.issued 2000-11
dc.identifier.uri http://hdl.handle.net/10016/10110
dc.description.abstract Kim and Pollard (1990) showed that a general class of M-estimators converge at rate nl/3 rather than at the standard rate n1/2 • Many times, this situation arises when the objective function is non-smooth. The limiting distribution is the (almost surely unique) random vector that maximizes a certain Gaussian process and is difficult to analyze analytically. In this paper, we propose the use of the subsampling method for inferential purposes. The general method is then applied to Manski' s maximum score estimator and its small sample performance is highlighted via a simulation study.
dc.format.mimetype application/octet-stream
dc.format.mimetype application/octet-stream
dc.format.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries UC3M Working papers. Statistics and Econometrics
dc.relation.ispartofseries 00-78
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Cube root asymptoties
dc.subject.other Maximum seore estimator
dc.subject.other Subsampling
dc.title Subsampling inference in cube root asymptotics with an application to manski's maximum score estimator
dc.type workingPaper
dc.subject.eciencia Estadística
dc.rights.accessRights openAccess
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