Show simple item record

1997-03-11Buch DOI: 10.18452/3810
Multivariate and Semiparametric Kernel Regression
dc.contributor.authorHärdle, Wolfgang
dc.contributor.authorMüller, Marlene
dc.date.accessioned2017-06-15T22:11:41Z
dc.date.available2017-06-15T22:11:41Z
dc.date.created2006-06-01
dc.date.issued1997-03-11
dc.identifier.issn1436-1086
dc.identifier.urihttp://edoc.hu-berlin.de/18452/4462
dc.description.abstractThe paper gives an introduction to theory and application of multivariate and semiparametric kernel smoothing. Multivariate nonparametric density estimation is an often used pilot tool for examining the structure of data. Regression smoothing helps in investigating the association between covariates and responses. We concentrate on kernel smoothing using local polynomial fitting which includes the Nadaraya-Watson estimator. Some theory on the asymptotic behavior and bandwidth selection is provided. In the applications of the kernel technique, we focus on the semiparametric paradigm. In more detail we describe the single index model (SIM) and the generalized partial linear model (GPLM).eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
dc.subject.ddc330 Wirtschaft
dc.titleMultivariate and Semiparametric Kernel Regression
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-10064120
dc.identifier.doihttp://dx.doi.org/10.18452/3810
dc.subject.dnb17 Wirtschaft
local.edoc.container-titleSonderforschungsbereich 373: Quantification and Simulation of Economic Processes
local.edoc.pages38
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume1997
local.edoc.container-issue26
local.edoc.container-year1997
local.edoc.container-erstkatid2135319-0

Show simple item record