Novel closed-form point estimators for a weighted exponential family derived from likelihood equations

TL;DR

This paper introduces new closed-form point estimators for weighted exponential families, enhances them with bootstrap bias reduction, and demonstrates their effectiveness through Monte Carlo simulations.

Contribution

The paper presents novel closed-form estimators for weighted exponential families and a bootstrap bias reduction technique, which improves estimation accuracy.

Findings

Bootstrap bias-reduced estimators outperform standard ones

Monte Carlo simulations confirm estimator effectiveness

Proposed methods are computationally efficient

Abstract

In this paper, we propose and investigate closed-form point estimators for a weighted exponential family. We also develop a bias-reduced version of these proposed closed-form estimators through bootstrap methods. Estimators are assessed using a Monte Carlo simulation, revealing favorable results for the proposed bootstrap bias-reduced estimators.