Numerous researches have been carried out to explain the relationship between the count data y and numbers of covariates x through a generalized linear model (GLM). This paper proposes a hierarchical Bayesian LASSO solution using six different prior models to the negative binomial regression. Latent variables Z have been introduced to simplify the GLM to a standard linear regression model. The ...

In actuarial hteramre, researchers suggested various statistical procedures to estimate the parameters in claim count or frequency model. In particular, the Poisson regression model, which is also known as the Generahzed Linear Model (GLM) with Poisson error structure, has been x~adely used in the recent years. However, it is also recognized that the count or frequency data m insurance practice...

Rate differences are an important effect measure in biostatistics and provide an alternative perspective to rate ratios. When the data are event counts observed during an exposure period, adjusted rate differences may be estimated using an identity-link Poisson generalised linear model, also known as additive Poisson regression. A problem with this approach is that the assumption of equality of...

Quasi-Poisson and negative binomial regression models have equal numbers of parameters, and either could be used for overdispersed count data. While they often give similar results, there can be striking differences in estimating the effects of covariates. We explain when and why such differences occur. The variance of a quasi-Poisson model is a linear function of the mean while the variance of...

This research aims to analyze the comparison of Poisson and Binomial Negative modeling causative factors gum abscess incidence in West Java. The methods used are Regression Negative. Predictors this habit eating sweet foods (X1), consumption sugary drinks (X2), never going a dental medical personnel (X3). results study obtained AIC values negative binomial models smaller than regression model. ...

Clinicians need to predict the number of involved nodes in breast cancer patients in order to ascertain severity, prognosis, and design subsequent treatment. The distribution of involved nodes often displays over-dispersion-a larger variability than expected. Until now, the negative binomial model has been used to describe this distribution assuming that over-dispersion is only due to unobserve...

Abstract One-parameter link functions play a fundamental role in regression via generalized linear modelling. This paper develops the general theory for two -parameter links very large class of vector models by using total derivatives applied to composite log-likelihood within Fisher scoring/iteratively reweighted least squares algorithm. We solve four-decade old problem with an interesting his...