Effective procedures for estimating beta distribution's parameters and Their confidence intervals

Abstract

First, we compare several methods for computing the ML estimators of the two-parameter beta distribution; the most effective one is identified. Second, a simple way is found to characterize the sampling distribution of the ML estimators; this characterization leads to a practical way of establishing confidence intervals for the ML estimators.

^*This research is partially supported by a grant from the University Center for Water Research, Oklahoma State University.

^*This research is partially supported by a grant from the University Center for Water Research, Oklahoma State University.

Notes

^*This research is partially supported by a grant from the University Center for Water Research, Oklahoma State University.