Computational Algorithms for the Estimation of Parameters on a Class of Beta Regression Models
A Study on Computational Algorithms in the Estimation of Parameters for the Class of Beta Regression Models
Beta regressions are widely used for modeling rates, ratios and proportions. We study computational aspects related to parameter
estimation of beta regressions by maximizing the log-likelihood function with heuristics and other optimization methods.
Through Monte Carlo simulations, we analyze the behavior of ten algorithms, where four of them present satisfactory results:
differential evolutionary, simulated annealing, stochastic ranking evolutionary, and controlled random search, with the latter
having the best performance. Using the four algorithms and the optim function of R, we study sets
of parameters that are hard to be estimated.
Codes and instances are provided in dir Etapa 1