Nonparametric density estimation for multivariate bounded data using two nonnegative multiplicative bias correction methods
| dc.contributor.author | Funke, Benedikt | |
| dc.contributor.author | Kawka, Rafael | |
| dc.date.accessioned | 2014-12-03T16:51:45Z | |
| dc.date.available | 2014-12-03T16:51:45Z | |
| dc.date.issued | 2014 | |
| dc.description.abstract | In this article we propose two new Multiplicative Bias Correction (MBC) techniques for nonparametric multivariate density estimation. We deal with positively supported data but our results can easily be ex- tended to the case of mixtures of bounded and unbounded supports. Both methods improve the optimal rate of convergence of the mean squared error up to O(n-8=(8+d)), where d is the dimension of the under- lying data set. Moreover, they overcome the boundary effect near the origin and their values are always non-negative. We investigate asymptotic properties like bias and variance as well as the performance of our estimators in Monte Carlo Simulations and in a real data example. | en |
| dc.identifier.uri | http://hdl.handle.net/2003/33762 | |
| dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-84 | |
| dc.language.iso | en | de |
| dc.relation.ispartofseries | Discussion Paper / SFB 823;39/2014 | |
| dc.subject | asymmetric kernels | en |
| dc.subject | multivariate density estimation | en |
| dc.subject | bias correction | en |
| dc.subject.ddc | 310 | |
| dc.subject.ddc | 330 | |
| dc.subject.ddc | 620 | |
| dc.title | Nonparametric density estimation for multivariate bounded data using two nonnegative multiplicative bias correction methods | en |
| dc.type | Text | de |
| dc.type.publicationtype | workingPaper | de |
| dcterms.accessRights | open access |