Resumen
When the analysis of real data indicates that normality assumptions are untenable, more flexible models that cope with the most prevalent deviations from normality can be adopted. In this context, the skew exponential power (SEP) distribution warrants special attention, because it encompasses distributions having both heavy tails and skewness, it allows likelihood inference, and it includes the normal model as a special case. This article concerns likelihood inference about the parameters of the SEP family. In particular, the information matrix of the maximum likelihood estimators (MLEs) is obtained and finite-sample properties of the estimators are investigated numerically. Special attention is given to the properties of the MLEs and likelihood ratio statistics when the data are drawn from a normal distribution, because this case is relevant for using the SEP distribution to test for normality. Application of the SEP distribution in robust estimation problems is considered for both independent and dependent data. Under moderate deviations from normality, estimators obtained under the SEP distribution are shown to outperform the normal-based estimators and to compete with robust estimators; furthermore, the SEP distribution offers the benefits of having a specified distribution, such as interpretable location and scale parameters under nonnormality.