Abstract: Two methods for clustering data and choosing a mixture model are proposed. First, we derive a new classification algorithm based on the classification likelihood. Then, the likelihood conditional on these clusters is written as the product of likelihoods of each cluster, and AIC- respectively BIC-type approximations are applied. The resulting criteria turn out to be the sum of the AIC or BIC relative to each cluster plus an entropy term. The performance of our methods is evaluated by Monte-Carlo methods and on a real data set, showing in particular that the iterative estimation algorithm converges quickly in general, and thus the computational load is rather low.
Abstract: This paper reviews zero-inflated count models and applies them to modelling annual trends in incidences of occupational allergic asthma, dermatitis and rhinitis in France. Based on the data collected from 2001 to 2009, the study uses the incidence rate ratios (IRR) as percentage of changes in incidences and plots them as function of the years to obtain trends. The investigation reveals that the trend is decreasing for asthma and rhinitis, and increasing for dermatitis, and that there is a possible positive association between the three diseases.