The classical integer valued first-order autoregressive (INA- R(1)) model has been defined on the basis of Poisson innovations. This model has Poisson marginal distribution and is suitable for modeling equidispersed count data. In this paper, we introduce an modification of the INAR(1) model with geometric innovations (INARG(1)) for model- ing overdispersed count data. We discuss some structu...

normal 0 false false false en-us x-none ar-sa microsoftinternetexplorer4 background: e pilepsy is a common, chronic neurological disorder that affects more than 40 million people worldwide. epilepsy is characterized by interictal and ictal functional disturbances. the presence of interictal epileptiform discharges (ieds) can help to confirm a clinical diagnosis of epilepsy, and their location a...

The aim of this paper is to study the probability to find an opportunity of collaboration among small and medium sized firms by participating in the Europartenariat meeting created to encourage co-operation links. Contacts among firms are relatively few in number and are assumed to be generated by a Poisson process. Empirical results of different Poisson regression models with reference to para...

This paper focuses on different methods of estimation and forecasting in first-order integer-valued autoregressive processes with Poisson-Lindley (PLINAR(1)) marginal distribution. For this purpose, the parameters of the model are estimated using Whittle, maximum empirical likelihood and sieve bootstrap methods. Moreover, Bayesian and sieve bootstrap forecasting methods are proposed and predict...

background: malaria is a major cause of morbidity and mortality in nepal. the magnitude of malaria across the country is alarming and varies with location. therefore, the present study aimed to model malaria incidence rates during 1998 to 2009 in nepal. methods: data for the study were obtained from health management information system (hmis), ministry of public health. a negative binomial mode...

We propose a two component graphical chain model, the discrete regression distribution, in which a set of categorical (or discrete) random variables is modeled as a response to a set of categorical and continuous covariates. We examine necessary and sufficient conditions for a discrete regression distribution to be described by a given graph. The discrete regression formulation is extended to a...

A two part probabilistic model for polycrystalline microstructures is described. The model utilizes a Poisson–Voronoi tessellation for the grain geometry and a vector random field model for the crystallographic orientation. The grain geometry model is calibrated to experimental data through the intensity of the Poisson point field underlying the Poisson–Voronoi tessellation and the orientation ...

The current paper surveys and develops numerical methods for multitype branching processes in continuous time. Particular attention is paid to the calculation of means, variances, extinction probabilities, and marginal distributions in the presence of a Poisson stream of immigrant particles. The Poisson process assumption allows for temporally complex patterns of immigration and facilitates app...

In this paper, we provide a method for modelling stationary time series. We allow the family of marginal densities for the observations to be speci ed. Our approach is to construct the model with a speci ed marginal family and build the dependence structure around it. We show that the resulting time series is linear with a simple autocorrelation structure. In particular, we present an original ...