The likelihood ratio spatial scan statistic has been widely used in spatial disease surveillance and spatial cluster detection applications. In order to better understand cluster mechanisms, an equivalent model-based approach is proposed to the spatial scan statistic that unifies currently loosely coupled methods for including ecological covariates in the spatial scan test. In addition, the uti...

Regression models are one of the most important used in modern studies, especially research and health studies because results they achieve. Two regression were used: Poisson Model Conway-Max Well- Poisson), where this study aimed to make a comparison between two choose best them using simulation method at different sample sizes (n = 25,50,100) with repetitions (r 1000). The Matlab program was ...

Poisson regression is a popular tool for modeling count data and is applied in a vast array of applications from the social to the physical sciences and beyond. Real data, however, are often overor under-dispersed and, thus, not conducive to Poisson regression. We propose a regression model based on the Conway–Maxwell-Poisson (COM-Poisson) distribution to address this problem. The COM-Poisson r...

Adaptive choice of smoothing parameters for nonparametric Poisson regression (O’Sullivan et. al., 1986) is considered in this paper. A computable approximation of the unbiased risk estimate (AUBR) for Poisson regression is introduced. This approximation can be used to automatically tune the smoothing parameter for the penalized likelihood estimator. An alternative choice is the generalized appr...

The inclusion of steric effects is important when determining the electrostatic potential near a solute surface. We consider a modified form of the Poisson-Boltzmann equation, often called the Poisson-Bikerman equation, in order to model these effects. The modifications lead to bounded ionic concentration profiles and are consistent with the Poisson-Boltzmann equation in the limit of zero-size ...

This paper develops a censored generalized Poisson regression model that can be used to predict a response variable that is a2ected by one or more explanatory variables. The censored generalized Poisson regression model is suitable for modeling count data that exhibit either overor under-dispersion. The regression parameters are estimated by the method of maximum likelihood and approximate test...

In this paper we present a review of some results about inference based on φ-divergence measures, under assumptions of multinomial sampling and loglinear models. The minimum φ-divergence estimator, which is seen to be a generalization of the maximum likelihood estimator is considered. This estimator is used in a φdivergence measure which is the basis of new statistics for solving three importan...

We consider L1-isotonic regression and L∞ isotonic and unimodal regression. For L1isotonic regression, we present a linear time algorithm when the number of outputs are bounded. We extend the algorithm to construct an approximate isotonic regression in linear time when the output range is bounded. We present linear time algorithms for L∞ isotonic and unimodal regression.

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regre...

In this paper we consider inference based on very general divergence measures, under assumptions of multinomial sampling and loglinear models. We define the minimum φ-divergence estimator, which is seen to be a generalization of the maximum likelihood estimator. This estimator is then used in a φ-divergence goodness-of-fit statistic, which is the basis of two new statistics for solving the prob...