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231226s2022 xx |||||o 00| ||eng c |
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|a 10.1080/02664763.2020.1843610
|2 doi
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|a pubmed24n1140.xml
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|a (DE-627)NLM342292021
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|a (NLM)35707813
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Ünlü, Hande Konşuk
|e verfasserin
|4 aut
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| 245 |
1 |
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|a A mixture model with Poisson and zero-truncated Poisson components to analyze road traffic accidents in Turkey
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|c 2022
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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|a Date Revised 16.07.2022
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|a published: Electronic-eCollection
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a © 2020 Informa UK Limited, trading as Taylor & Francis Group.
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|a The analysis of traffic accident data is crucial to address numerous concerns, such as understanding contributing factors in an accident's chain-of-events, identifying hotspots, and informing policy decisions about road safety management. The majority of statistical models employed for analyzing traffic accident data are logically count regression models (commonly Poisson regression) since a count - like the number of accidents - is used as the response. However, features of the observed data frequently do not make the Poisson distribution a tenable assumption. For example, observed data rarely demonstrate an equal mean and variance and often times possess excess zeros. Sometimes, data may have heterogeneous structure consisting of a mixture of populations, rather than a single population. In such data analyses, mixtures-of-Poisson-regression models can be used. In this study, the number of injuries resulting from casualties of traffic accidents registered by the General Directorate of Security (Turkey, 2005-2014) are modeled using a novel mixture distribution with two components: a Poisson and zero-truncated-Poisson distribution. Such a model differs from existing mixture models in literature where the components are either all Poisson distributions or all zero-truncated Poisson distributions. The proposed model is compared with the Poisson regression model via simulation and in the analysis of the traffic data
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|a Journal Article
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4 |
|a Count data
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| 650 |
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4 |
|a EM algorithm
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| 650 |
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4 |
|a finite mixture models
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| 650 |
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4 |
|a identifiability
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| 650 |
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4 |
|a zero-truncated Poisson
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| 700 |
1 |
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|a Young, Derek S
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yiğiter, Ayten
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Hilal Özcebe, L
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t Journal of applied statistics
|d 1991
|g 49(2022), 4 vom: 17., Seite 1003-1017
|w (DE-627)NLM098188178
|x 0266-4763
|7 nnns
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| 773 |
1 |
8 |
|g volume:49
|g year:2022
|g number:4
|g day:17
|g pages:1003-1017
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| 856 |
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|u http://dx.doi.org/10.1080/02664763.2020.1843610
|3 Volltext
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