Nonparametric M-estimation with long-memory errors
dc.contributor.author | Ghosh, Sucharita | de |
dc.contributor.author | Sibbertsen, Philipp | de |
dc.date.accessioned | 2004-12-06T18:42:49Z | |
dc.date.available | 2004-12-06T18:42:49Z | |
dc.date.issued | 2000 | de |
dc.description.abstract | We investigate the behavior of nonparametric kernel M-estimators in the presence of long-memory errors. The optimal bandwidth and a central limit theorem are obtained. It turns out that in the Gaussian case all kernel M-estimators have the same limiting normal distribution. The motivation behind this study is illustrated with an example. | de |
dc.format.extent | 244852 bytes | |
dc.format.extent | 308145 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | application/postscript | |
dc.identifier.uri | http://hdl.handle.net/2003/5041 | |
dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-15124 | |
dc.language.iso | en | de |
dc.publisher | Universitätsbibliothek Dortmund | de |
dc.subject.ddc | 310 | de |
dc.title | Nonparametric M-estimation with long-memory errors | en |
dc.type | Text | de |
dc.type.publicationtype | report | en |
dcterms.accessRights | open access |