We study the general problem of estimating a ‘hidden’ point process X given the realisation of an ‘observed’ point process Y (possibly defined in different spaces) with known joint distribution. We characterise the posterior distribution of X under marginal Poisson and Gauss-Poisson prior and when the transformation from X to Y includes thinning, displacement and augmentation with extra points....

on comparing responses to pairs of items irrespective of other items. The pseudo-likelihood method is comparable to Fischer’s (1974) Minchi method. A simulation study found that the pseudo-likelihood estimates and their (estimated) standard errors were comparable to conditional and marginal maximum likelihood estimates. The method is extended to estimate parameters of the linear logistic test m...

We show that if the Banach-Mazur distance between an n-dimensional normed space X and l∞ is at most 3/2, then there exist n+ 1 equidistant points in X. By a well-known result of Alon and Milman, this implies that an arbitrary n-dimensional normed space admits at least e √ log n equidistant points, where c > 0 is an absolute constant. We also show that there exist n equidistant points in spaces ...

The Poisson-Galton-Watson distribution on nite trees, and the related PGW 1 (1) distribution on innnite trees with one end, arise in several contexts, in particular as n ! 1 weak limits within various size-n combinato-rial models. We review this topic, introducingslick notation for describing such distributions. We then describe a family of continuous-time Markov chains whose marginal distribut...

In this work we propose a model-based clustering method for time series. The model uses an almost surely discrete Bayesian nonparametric prior to induce clustering of the series. Specifically we propose a general Poisson-Dirichlet process mixture model, which includes the Dirichlet process mixture model as particular case. The model accounts for typical features present in a time series like tr...

Generalized linear mixed models are flexible tools for modeling non-normal data and are useful for accommodating overdispersion in Poisson regression models with random effects. Their main difficulty resides in the parameter estimation because there is no analytic solution for the maximization of the marginal likelihood. Many methods have been proposed for this purpose and many of them are impl...

We introduced a random vector , where has Poisson distribution and are minimum of independent and identically distributed exponential random variables. We present fundamental properties of this vector such as PDF, CDF and stochastic representations. Our results include explicit formulas for marginal and conditional distributions, moments and moments generating functions. We also derive moments ...