A generalized right truncated bivariate Poisson regression model is proposed in this paper. Estimation and tests for goodness of fit and over or under dispersion are illustrated for both untruncated and right truncated bivariate Poisson regression models using marginal-conditional approach. Estimation and test procedures are illustrated for bivariate Poisson regression models with applications ...

In this paper we seek to understand the relationship between the online popularity of an article and its intrinsic quality. Prior experimental work suggests that the relationship between quality and popularity can be very distorted due to factors like social influence bias and inequality in visibility. We conduct a study of popularity on two different social news aggregators, Reddit and Hacker ...

[1] The loads regression estimator (LRE) was introduced by Wang et al. (2011) as an improved approach for quantifying the export of loads and the corresponding uncertainty from river systems, where data are limited. We extend this methodology and show how LRE can be used to analyze a 24 year record of total suspended sediment concentrations for the Burdekin River. For large catchments with high...

Bayesian inference about small areas is of considerable current interest, and simultaneous intervals for the parameters for the areas are needed because these parameters are correlated. This is not usually pursued because with many areas the problem becomes difficult. We describe a method for finding simultaneous credible intervals for a relatively large number of parameters, each corresponding...

0 Introduction. A Cmanifold M is endowed by a Poisson pair if two linearly independent smooth bivectors c1, c2 are defined on M and cλ = λ1c1 + λ2c2 is a Poisson bivector for any λ = (λ1, λ2) ∈ R . A bihamiltonian structure J = {cλ} is the whole 2-dimensional family of bivectors. The structure J is degenerate if rank cλ < dim M,λ ∈ R. An intensive study of such objects was done by I.M.Gelfand a...

The work contains computer-aided method of contruction of multifactor and complicated models on the basis of experimental data. The models are non-linear but traditional (constructed with the help of the method with the preliminary transformation of the matrix of the initial data and the subsequent reverse transformation of the results). The models can have different form linear, multiplicative...

The most common probabilistic structure of the models used by transportation safety analysts for modeling motor vehicle crashes are the traditional Poisson and Poissongamma (or Negative Binomial) distributions. Since crash data have been shown to exhibit over-dispersion, Poisson-gamma models are usually preferred over Poisson regression models. Up until recently, the dispersion parameter of Poi...

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...

A new area of research involves the use of normalized and scaled Google search volume data to predict economic activity. This new source of data holds both many advantages as well as disadvantages. Daily and weekly data are employed to show the effect of aggregation in Google data, which can lead to contradictory findings. In this paper, Poisson regressions are used to explore the relationship ...

We consider the application of a popular penalised regression method, Ridge Regression, to data with very high dimensions and many more covariates than observations. Our motivation is the problem of out-of-sample prediction and the setting is high-density genotype data from a genome-wide association or resequencing study. Ridge regression has previously been shown to offer improved performance ...