Approaching the parameter estimation quality of maximum likelihood via generalized moments

TL;DR

This paper introduces a practical criterion for constructing generalized moments that can nearly achieve the Rao-Cramer limit in parameter estimation, offering a simpler alternative to maximum likelihood for complex distributions and large datasets.

Contribution

It proposes a new criterion for designing generalized moments that approximate maximum likelihood efficiency without its computational complexity.

Findings

Generalized moments can approach the Rao-Cramer limit.

The method simplifies parameter estimation for complex distributions.

Applicable to large sample sizes and complicated probability models.

Abstract

A simple criterion is presented for a practical construction of generalized moments that allow one to approach the theoretical Rao-Cramer limit for parameter estimation while avoiding the complexity of the maximum likelihood method in the cases of complicated probability distributions and/or very large event samples.