dc.contributor.author |
Chew-Seng, Chee |
|
dc.date.accessioned |
2012-07-23T07:04:33Z |
|
dc.date.available |
2012-07-23T07:04:33Z |
|
dc.date.issued |
2011 |
|
dc.identifier.uri |
http://hdl.handle.net/123456789/1621 |
|
dc.description.abstract |
The primary goal of this thesis is to provide a mixture-based framework for nonparametric density estimation. This framework advocates the use of a mixture
model with a nonparametric mixing distribution to approximate the distribution of the data. The implementation of a mixture-based nonparametric density estimator
generally requires the specification of parameters in a mixture model and the choice of the bandwidth parameter. Consequently, a nonparametric methodology consisting of both the estimation and selection steps is described. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
New Zealand: University of Auckland |
en_US |
dc.relation.ispartofseries |
;QA 278.8 .C4 2011 |
|
dc.subject |
QA 278.8 .C4 2011 |
en_US |
dc.subject |
Chew-Seng, Chee |
en_US |
dc.subject |
Tesis University of Auckland 2011 |
en_US |
dc.subject |
Nonparametric statistics -- Research |
en_US |
dc.title |
A mixture- based framework for nonparametric density estimation |
en_US |
dc.type |
Thesis |
en_US |