{"title":"A generalised Dickman distribution and the number of species in a negative binomial process model","authors":"Yuguang F. Ipsen, R. Maller, S. Shemehsavar","doi":"10.1017/apr.2020.61","DOIUrl":null,"url":null,"abstract":"Abstract We derive the large-sample distribution of the number of species in a version of Kingman’s Poisson–Dirichlet model constructed from an \n$\\alpha$\n -stable subordinator but with an underlying negative binomial process instead of a Poisson process. Thus it depends on parameters \n$\\alpha\\in (0,1)$\n from the subordinator and \n$r>0$\n from the negative binomial process. The large-sample distribution of the number of species is derived as sample size \n$n\\to\\infty$\n . An important component in the derivation is the introduction of a two-parameter version of the Dickman distribution, generalising the existing one-parameter version. Our analysis adds to the range of Poisson–Dirichlet-related distributions available for modeling purposes.","PeriodicalId":0,"journal":{"name":"","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/apr.2020.61","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1017/apr.2020.61","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Abstract We derive the large-sample distribution of the number of species in a version of Kingman’s Poisson–Dirichlet model constructed from an
$\alpha$
-stable subordinator but with an underlying negative binomial process instead of a Poisson process. Thus it depends on parameters
$\alpha\in (0,1)$
from the subordinator and
$r>0$
from the negative binomial process. The large-sample distribution of the number of species is derived as sample size
$n\to\infty$
. An important component in the derivation is the introduction of a two-parameter version of the Dickman distribution, generalising the existing one-parameter version. Our analysis adds to the range of Poisson–Dirichlet-related distributions available for modeling purposes.