This study integrates association rules analysis, a methodology for selecting potential interactions, with Poisson regression modeling. Though typically ignored in conventional Poisson regression, interactions are very common in practice. However, selecting a Poisson regression model when many main effects and interactions are involved is problematic. In this study, we develop a model selection...

Cellular electrophysiology is often modeled using the cable equations. The cable model can only be used when ionic concentration effects and three dimensional geometry effects are negligible. The Poisson model, in which the electrostatic potential satisfies the Poisson equation and the ionic concentrations satisfy the drift-diffusion equation, is a system of equations that can incorporate such ...

The generalized Poisson regression model has been used to model dispersed count data. It is a good competitor to the negative binomial regression model when the count data is over-dispersed. Zero-inflated Poisson and zero-inflated negative binomial regression models have been proposed for the situations where the data generating process results into too many zeros. In this paper, we propose a z...

The number of the unique individuals in the population is of great importance in evaluating the disclosure risk of a microdata set. We approach this problem by considering some basic superpopulation models including the gamma-Poisson model of Bethlehem et al. (1990). We introduce Dirichlet-multinomial model which is closely related but more basic than the gamma-Poisson model, in the sense that ...

We present a probability distribution over non-negative integer valued matrices with possibly an infinite number of columns. We also derive a stochastic process that reproduces this distribution over equivalence classes. This model can play the role of the prior in nonparametric Bayesian learning scenarios where multiple latent features are associated with the observed data and each feature can...

In the random graph G(n, p) with pn bounded, the degrees of the vertices are almost i.i.d Poisson random variables with mean λ := p(n − 1). Motivated by this fact, we introduce the Poisson cloning model GPC(n, p) for random graphs in which the degrees are i.i.d Poisson random variables with mean λ. Then, we first establish a theorem that shows the new model is equivalent to the classical model ...

Periodicity and Ruin Probabilities for Compound Non-Homogenous Poisson Processes Compound non-homogenous Poisson processes with periodic claim intensity rates are stiidied in this work. A risk process related to a short term periodic environment and the periodicity for its compound claim counting process are discussed. The ruin probabilities of compo~md non-homogenous Poisson processes with per...

Harter’s 2-Poisson model of Information Retrieval is a univariate model of the raw term frequencies, that does not condition the probabilities on document length [2]. A bivariate stochastic model is thus introduced to extend Harter’s 2-Poisson model, by conditioning the term frequencies of the document to the document length. We assume Harter’s hypothesis: the higher the probability f(X = x|L =...

Multivariate count models are rare in political science, despite the presence of many count time series. This article develops a new Bayesian Poisson vector autoregression (BaP-VAR) model that can characterize endogenous dynamic counts with no restrictions on the contemporaneous correlations. Impulse responses, decomposition of the forecast errors, and dynamic multiplier methods for the effects...

In the random graph G(n, p) with pn bounded, the degrees of the vertices are almost i.i.d. Poisson random variables with mean λ := p(n− 1). Motivated by this fact, we introduce the Poisson cloning model GPC(n, p) for random graphs in which the degrees are i.i.d. Poisson random variables with mean λ. We first establish a theorem that shows that the new model is equivalent to the classical model ...