RESUMO
We propose a new technique for the study of multivariate count data. The proposed model is applied to the study of the number of individuals several fossil species found in a set of geographical observation points. First, we are proposing a multivariate model based on the Poisson distributions, which allows positive and negative correlations between the components. We are extending the log-linear Poisson model in the multivariate case through the conditional distributions. For this model, we obtain the maximum likelihood estimates and compute several goodness of fit statistics. Finally we illustrate the application of the proposed method over data sets: various simulated data sets and a count data set of various fossil species.
RESUMO
Length of hospital stay (LOS) is an important indicator of the hospital activity and management of health care. The skewness exhibited by this variable poses problems in statistical modeling. The aim of this work is to model the variable LOS within diagnosis-related groups (DRG) through finite mixtures of distributions. A mixture of the union of Gamma, Weibull and Lognormal families is used in the model, instead of a mixture of a unique distribution family. Some theoretical questions regarding the model, such as the identifiability and study of asymptotic properties of ML estimators, are analyzed. The EM algorithm is proposed for performing these estimators. Finally, this new proposed model is illustrated by using data from different DRGs.
