Bias-corrected maximum likelihood estimation for the beta distribution

This paper gives closed-form expressions for bias-corrected maximum likelihood estimates of the parameters of the beta distribution that can be used to define bias-corrected estimates that are nearly unbiased. Some approximations based on asymptotic expansions for the bias corrections are given. We also present simulation results comparing the performances of the maximum likelihood estimates and corrected ones. The results suggest that bias-corrected estimates have better finite-sample performance than standard maximum likelihood estimates.