Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/20082
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dc.contributor.authorSchnedler, Wendelinen_US
dc.date.accessioned2009-01-28T16:11:28Z-
dc.date.available2009-01-28T16:11:28Z-
dc.date.issued2003en_US
dc.identifier.urihttp://hdl.handle.net/10419/20082-
dc.description.abstractThis article considers a wide class of censoring problems and presents a construction rule for an objective function. This objective function generalises the ordinary likelihood as well as particular ?likelihoods? used for estimation in several censoring models. Under regularity conditions the maximiser of this generalised likelihood has all the properties of a maximumlikelihood estimator: it is consistent and the respective root-n estimator is asymptotically efficient and normally distributed.en_US
dc.language.isoengen_US
dc.publisher|aInstitute for the Study of Labor (IZA) |cBonnen_US
dc.relation.ispartofseries|aIZA Discussion paper series |x837en_US
dc.subject.jelC13en_US
dc.subject.jelC24en_US
dc.subject.ddc330en_US
dc.subject.keywordcensored variablesen_US
dc.subject.keywordM-estimationen_US
dc.subject.keywordmultivariate methodsen_US
dc.subject.keywordrandom censoringen_US
dc.subject.keywordgeneralised likelihooden_US
dc.subject.stwTobit-Modellen_US
dc.subject.stwSchätztheorieen_US
dc.subject.stwTheorieen_US
dc.titleWhat You Always Wanted to Know About Censoring But Never Dared to Ask - Parameter Estimation for Censored Random Vectorsen_US
dc.typeWorking Paperen_US
dc.identifier.ppn367381028en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-

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