BACKGROUND Traditional statistical models often are based on certain presuppositions and limitations that may not presence in actual data and lead to turbulence in estimation or prediction. In these situations, artificial neural networks (ANNs) could be suitable alternative rather than classical statistical methods. STUDY DESIGN  A prospective cohort study. METHODS The study was conducted i...

In Generalized Linear Models, likelihood equations are intractable and do not have explicit solutions; thus, they must be solved by using Newton-type algorithms. Solving these equations by iterations, however, can be problematic: the iterations might converge to wrong values or the iterations might not converge at all. In this study, we derive the modified maximum likelihood estimators for Pois...

A number of methods have been proposed for dealing with extra-Poisson variation when doing regression analysis of count data. This paper studies negative-binomial regression models and examines efficiency and robustness properties of inference procedures based on them. The methods are compared with quasilikelihood methods. RESUME Plusieurs mkthodes ont ktk propokes en vue de traiter le probltme...

The Poisson regression model is frequently used to analyze count data. Pseudo R-squared measures for Poisson regression models have recently been proposed and bias adjustments recommended in the presence of small samples and/or a large number of covariates. In practice, however, data are often overor sometimes even underdispersed as compared to the standard Poisson model. The de5nition of Poiss...

In Generalized Linear Models, likelihood equations are intractable and do not have explicit solutions; thus, they must be solved by using Newton-type algorithms. Solving these equations by iterations, however, can be problematic: the iterations might converge to wrong values or the iterations might not converge at all. In this study, we derive the modified maximum likelihood estimators for Pois...

Excess zeros and overdispersion are commonly encountered phenomena that limit the use of traditional Poisson regression models for modeling count data. The focus of this paper is on modeling count data in the case that a population has excess zero counts and also consists of several sub-populations in the non-zero counts. The proposed zero-inflated Poisson regression mixture model accounts for ...

A semiparametric Poisson regression is proposed in modeling spatially clustered count data. The heterogeneous covariate effect across the clusters is formulated in the context of nonparametric regression while the random clustering effect is based on a parametric specification. We propose two estimation procedures: (1) the parametric and nonparametric parts are estimated simultaneously via pena...