Sieve maximum likelihood estimation in a semi-parametric regression model with errors in variables

dc.contributor.author	Belomestny, Denis
dc.contributor.author	Klochkov, Egor
dc.contributor.author	Spokoiny, Vladimir
dc.date.accessioned	2016-04-12T13:01:21Z
dc.date.available	2016-04-12T13:01:21Z
dc.date.issued	2016
dc.description.abstract	The paper deals with a semi-parametric regression problem under deterministic and regular design which is observed with errors. We first linearise the problem using a sieve approach and then apply the total penalised maximum likelihood estimator to the linearised model. Sufficient conditions for √n-consistency and efficiency under parametric assumption are derived and a possible misspecification bias under different smoothness assumptions on the design is analysed. The Monte Carlo simulations show the performance of the estimator with simulated data.	en
dc.identifier.uri	http://hdl.handle.net/2003/34888
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-16936
dc.language.iso	en	de
dc.relation.ispartofseries	Discussion Paper / SFB823;15, 2016	en
dc.subject	errors-in-variables model	en
dc.subject	√n-consistency	en
dc.subject	regression	en
dc.subject.ddc	310
dc.subject.ddc	330
dc.subject.ddc	620
dc.title	Sieve maximum likelihood estimation in a semi-parametric regression model with errors in variables	en
dc.type	Text	de
dc.type.publicationtype	workingPaper	de
dcterms.accessRights	open access

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