Generalized Poisson Hurdle Model for Count Data and Its Application in Ear Disease

For count data, though a zero-inflated model can work perfectly well with an excess of zeroes and the generalized Poisson model can tackle over- or under-dispersion, most models cannot simultaneously deal with both zero-inflated or zero-deflated data and over- or under-dispersion. Ear diseases are important in healthcare, and falls into this kind of count data. This paper introduces a generalized Poisson Hurdle model that work with count data of both too many/few zeroes and a sample variance not equal to the mean. To estimate parameters, we use the generalized method of moments. In addition, the asymptotic normality and efficiency of these estimators are established. Moreover, this model is applied to ear disease using data gained from the New South Wales Health Research Council in 1990. This model performs better than both the generalized Poisson model and the Hurdle model.     

Fitting the extended poisson process model to grouped binary data

Summary  Extended Poisson process modelling allows the construction of a broad class of distributions, including distributions over-dispersed or under-dispersed relative to the binomial distribution, with the binomial distribution being a special case. In this paper an iteratively re-weighted least squares algorithm for fitting such generalised binomial distributions is presented, and is illustrated with an example.     

纯生过程的变异性(英语)

设{X(t):t≥0}为零初值纯生过程,出生率为λ_n,n≥0.在本文中,我们证明了Faddy[7]的一个猜测:当出生率为单调增加序列λ_0≤λ_1≤λ_2…。时,Var{X(t)}≥E{(t)};当出生率为单调减少序列时Var{X(t)}≤E{(t)}。