Abstract
On the basis of an interesting and tractable parametric family of distributions, which figures prominently as an empirical model for asymmetric and heavy-tailed data, we introduce a novel parametric regression model that is quite simple and may be very useful to model many types of real data which occurs frequently in practice. The unknown parameters are estimated using the maximum likelihood estimation method, and Monte Carlo experiments indicate that this traditional approach works properly to estimate the unknown parameters. Diagnostic measures (normalized quantile residuals, and global and local influence methods) are also discussed for the new parametric regression model. Empirical applications are considered, and comparisons with three of the most popular existing regression models are made.
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