Journal of Data Science

Abstract: Normality (symmetric) of the random effects and the within subject errors is a routine assumptions for the linear mixed model, but it may be unrealistic, obscuring important features of among- and within-subjects variation. We relax this assumption by considering that the random effects and model errors follow a skew-normal distributions, which includes normal ity as a special case and provides flexibility in capturing a broad range of non-normal behavior. The marginal distribution for the observed quantity is derived which is expressed in closed form, so inference may be carried out using existing statistical software and standard optimization techniques. We also implement an EM type algorithm which seem to provide some ad vantages over a direct maximization of the likelihood. Results of simulation studies and applications to real data sets are reported.

Abstract: As an extension to previous research efforts, the PPM is applied to the identification of multiple change points in the parameter that indexes the regular exponential family. We define the PPM for Yao’s prior cohesions and contiguous blocks. Because the exponential family provides a rich set of models, we also present the PPM for some particular members of this family in both continuous and discrete cases and the PPM is applied to identify multiple change points in real data. Firstly, multiple changes are identified in the rates of crimes in one of the biggest cities in Brazil. In order to illustrate the continuous case, multiple changes are identified in the volatility (variance) and in the expected return (mean) of some Latin America emerging markets return series.