Characterizations of the maximum likelihood estimator of the Cauchy distribution

TL;DR

This paper introduces a novel complex-parameter approach to maximum likelihood estimation for the Cauchy distribution, deriving new iterative and algebraic schemes, including polynomial root-finding, and demonstrates their effectiveness through numerical examples.

Contribution

It presents a new complex-variable formulation and algebraic methods for MLE of the Cauchy distribution, revealing the non-existence of closed-form solutions for sample size five.

Findings

New iterative scheme for MLE approximation

Polynomial formulation for MLE estimation

Numerical validation of the proposed methods

Abstract

This paper gives a new approach for the maximum likelihood estimation of the joint of the location and scale of the Cauchy distribution. We regard the joint as a single complex parameter and derive a new form of the likelihood equation of a complex variable. Based on the equation, we provide a new iterative scheme approximating the maximum likelihood estimate. We also handle the equation in an algebraic manner and derive a polynomial containing the maximum likelihood estimate as a root. This algebraic approach provides another scheme approximating the maximum likelihood estimate by root-finding algorithms for polynomials, and furthermore, gives non-existence of closed-form formulae for the case that the sample size is five. We finally provide some numerical examples to show our method is effective.