  Beta regression has been extensively used by statisticians and practitioners
to model bounded continuous data and there is no strong and similar competitor
having its main features. A class of normalized inverse-Gaussian (N-IG) process
was introduced in the literature, being explored in the Bayesian context as a
powerful alternative to the Dirichlet process. Until this moment, no attention
has been paid for the univariate N-IG distribution in the classical inference.
In this paper, we propose the bessel regression based on the univariate N-IG
distribution, which is a robust alternative to the beta model. This robustness
is illustrated through simulated and real data applications. The estimation of
the parameters is done through an Expectation-Maximization algorithm and the
paper discusses how to perform inference. A useful and practical discrimination
procedure is proposed for model selection between bessel and beta regressions.
Monte Carlo simulation results are presented to verify the finite-sample
behavior of the EM-based estimators and the discrimination procedure. Further,
the performances of the regressions are evaluated under misspecification, which
is a critical point showing the robustness of the proposed model. Finally,
three empirical illustrations are explored to confront results from bessel and
beta regressions.
