  In this paper, novel closed-form point estimators of the beta distribution
are proposed and investigated. The first estimators are a modified version of
Pearson's method of moments. The underlying idea is to involve the sufficient
statistics, i.e., log-moments in the moment estimation equations and solve the
mixed type of moment equations simultaneously. The second estimators are based
on an approximation to Fisher's likelihood principle. The idea is to solve two
score equations derived from the log-likelihood function of generalized beta
distributions. Both two resulted estimators are in closed forms, strongly
consistent and asymptotically normal. In addition, through extensive
simulations, the proposed estimators are shown to perform very close to the ML
estimators in both small and large samples, and they significantly outperform
the moment estimators.
