Título: Multivariate linear regression models for asymmetric and heavy-tailed data
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Resumo: Multivariate linear regression models are formed by a vector of variables of interest/responses, a set of explanatory variables, a linear predictor formed by a linear combination of these explanatory variables and regression coefficients, and a random component that flexibilities the relationship systematic and the responses vector. Various experimental or observed phenomena in nature generate data with asymmetric behavior and/or heavy tails, such as phenotypic measurements in athletes, rainfall, among others. Thus, the usual hypothesis of normality of the data is relaxed using a more general class of distributions that incorporate asymmetry and heavy tails and have in particular cases the normal distribution, as well as other symmetrical/asymmetric distributions. In this lecture, we will approach some of these classes of distributions highlighting their properties, methods of parameter estimation, and applications in real data.
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