@article { ISI:000268567000006,
	title = {Modelling heavy-tailed count data using a generalised Poisson-inverse Gaussian family},
	journal = {Statistics \& Probability Letters},
	volume = {79},
	number = {15},
	year = {2009},
	month = {AUG 1},
	pages = {1695-1703},
	publisher = {Elsevier Science BV},
	type = {Article},
	abstract = {We generalise the Poisson-inverse Gaussian distribution to a three-parameter family, which includes the Poisson and discrete stable distributions as boundary cases. It is flexible in modelling Count data sets with different tail heaviness. Although the family only has a closed-form probability generating function, a recursive method is developed for statistical inferences based on the likelihood. As an example, this new family is applied to data sets of citation counts of published articles. (C) 2009 Elsevier B.V. All rights reserved.},
	issn = {0167-7152},
	doi = {10.1016/j.spl.2009.04.011},
	author = {Zhu, Rong and Joe, Harry}
}
