J. Japan Statist. Soc., Vol. 33 (No. 1), pp. 105-117, 2003

Random Clustering Based on the Conditional Inverse Gaussian-Poisson Distribution

Nobuaki Hoshino

Abstract. The present article describes a Conditional Inverse Gaussian-Poisson (CIGP) distribution, obtained by conditioning an inverse Gaussian-Poisson population model on its total frequency. This CIGP distribution is equivalent to random partitioning of positive integers, with the possibility for a number of applications in statistical ecology, linguistics and statistical disclosure control to name a few. After showing the marginal moments of the distribution, parameter estimation is discussed. Fitting the CIGP distribution to some typical data sets demonstrates its applicability.

Key words and phrases: Disclosure risk, frequencies of frequencies, size index, species abundance, superpopulation.

[Full text] (PDF 148 KB)