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dc.contributor.authorChew-Seng, Chee-
dc.date.accessioned2017-04-09T05:10:07Z-
dc.date.available2017-04-09T05:10:07Z-
dc.date.issued2015-09-18-
dc.identifier.citationVol.100(3);239-257p.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/5391-
dc.description.abstractNonparametric 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.isoenen_US
dc.publisherAStA Advances in Statistical Analysisen_US
dc.titleModelling Of Count Data Using Nonparametric Mixturesen_US
dc.typeArticleen_US
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