Modelos elípticos multiníveis

Multilevel models represent a class of models used to adjust data which have hierarchical structure. The present work proposes a generalization of the multilevel normal models, named multilevel elliptical models. This proposal suggests the use of probability distributions belonging to the elliptical class, thus involving all symmetric continuous distributions, including the normal distribution as a particular case. Elliptical distributions may have lighter or heavier tails than the normal ones. In case of presence of outlying observations, it is suggested the use of heavy-tailed distributions in order to obtain a better fitted model to the discrepant observations. In this dissertation some aspects of the multilevel elliptical models are developed, such as the process of parameter estimation by maximum likelihood, hypothesis tests for fixed effects and variance-covariance parameters and residual analysis to check features related to the fitting and established assumptions.