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DC Field | Value | Language |
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dc.contributor.author | Chew-Seng, Chee | - |
dc.date.accessioned | 2017-04-09T05:10:07Z | - |
dc.date.available | 2017-04-09T05:10:07Z | - |
dc.date.issued | 2015-09-18 | - |
dc.identifier.citation | Vol.100(3);239-257p. | en_US |
dc.identifier.uri | http://hdl.handle.net/123456789/5391 | - |
dc.description.abstract | Nonparametric modelling of count data is partly motivated by the fact that using parametric count models not only runs the risk of model misspecification but also is rather restrictive in terms of local approximation. Accordingly, we present a framework of using nonparametric mixtures for flexible modelling of count data. We consider the use of the least squares function in nonparametric mixture modelling and provide two algorithms for least squares fitting of nonparametric mixtures. Two illustrations of the framework are given, each with a particular nonparametricmixture. One illustration is the use of the nonparametric Poisson mixture for general modelling purposes. | en_US |
dc.language.iso | en | en_US |
dc.publisher | AStA Advances in Statistical Analysis | en_US |
dc.title | Modelling Of Count Data Using Nonparametric Mixtures | en_US |
dc.type | Article | en_US |
Appears in Collections: | Journal Articles |
Files in This Item:
File | Description | Size | Format | |
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98-Modelling_of_count_data_using_nonparametric_mixtures.png | Evidence | 172.2 kB | image/png | View/Open |
J2016-98-Modelling of count data using nonparametric mixtures.pdf | Fulltext File | 714.68 kB | Adobe PDF | View/Open |
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