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A mixture- based framework for nonparametric density estimation

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...

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Main Author: Chew-Seng, Chee
Format: Thesis
Language:English
Published: New Zealand: University of Auckland 2012
Subjects:
QA 278.8 .C4 2011
Chew-Seng, Chee
Tesis University of Auckland 2011
Nonparametric statistics > Research
Online Access:http://hdl.handle.net/123456789/1621
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id my.umt.ir-1621
record_format eprints
spelling my.umt.ir-16212012-07-23T07:04:33Z A mixture- based framework for nonparametric density estimation Chew-Seng, Chee QA 278.8 .C4 2011 Chew-Seng, Chee Tesis University of Auckland 2011 Nonparametric statistics -- Research 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. 2012-07-23T07:04:33Z 2012-07-23T07:04:33Z 2011 Thesis http://hdl.handle.net/123456789/1621 en ;QA 278.8 .C4 2011 application/pdf application/pdf New Zealand: University of Auckland
institution Universiti Malaysia Terengganu
building Perpustakaan Sultanah Nur Zahirah
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Terengganu
content_source UMT-IR
url_provider http://umt-ir.umt.edu.my:8080/
language English
topic QA 278.8 .C4 2011
Chew-Seng, Chee
Tesis University of Auckland 2011
Nonparametric statistics -- Research
spellingShingle QA 278.8 .C4 2011
Chew-Seng, Chee
Tesis University of Auckland 2011
Nonparametric statistics -- Research
Chew-Seng, Chee
A mixture- based framework for nonparametric density estimation
description 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.
format Thesis
author Chew-Seng, Chee
author_facet Chew-Seng, Chee
author_sort Chew-Seng, Chee
title A mixture- based framework for nonparametric density estimation
title_short A mixture- based framework for nonparametric density estimation
title_full A mixture- based framework for nonparametric density estimation
title_fullStr A mixture- based framework for nonparametric density estimation
title_full_unstemmed A mixture- based framework for nonparametric density estimation
title_sort mixture- based framework for nonparametric density estimation
publisher New Zealand: University of Auckland
publishDate 2012
url http://hdl.handle.net/123456789/1621
_version_ 1738395409135435776
score 13.214268

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