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

Simple item page
dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorDelgado, Miguel A.
dc.contributor.authorRodríguez Poo, Juan M.
dc.contributor.authorWolf, Michael
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Estadísticaes
dc.date.accessioned2011-01-26T17:25:11Z
dc.date.available2011-01-26T17:25:11Z
dc.date.issued2000-11
dc.description.abstractKim 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.es
dc.format.mimetypeapplication/octet-stream
dc.format.mimetypeapplication/octet-stream
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10016/10110
dc.language.isoenges
dc.relation.ispartofseriesUC3M Working papers. Statistics and Econometricses
dc.relation.ispartofseries00-78es
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.ecienciaEstadísticaes
dc.subject.otherCube root asymptotieses
dc.subject.otherMaximum seore estimatores
dc.subject.otherSubsamplinges
dc.titleSubsampling inference in cube root asymptotics with an application to manski's maximum score estimatores
dc.typeworking paper*
dspace.entity.typePublication
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
ws0078.pdf
Size:
302.51 KB
Format:
Adobe Portable Document Format
Download
Collections
DES - Working Papers. Statistics and Econometrics. WS