Robust inference in inflated beta regression

TL;DR

This paper introduces robust estimation methods for inflated beta regression models to improve inference accuracy in the presence of outliers, maintaining model interpretability and providing practical algorithms.

Contribution

It proposes new robust estimators and algorithms for inflated beta regression, along with robust tests, enhancing inference reliability over traditional maximum likelihood methods.

Findings

Robust estimators outperform MLE in the presence of outliers.

Simulation studies demonstrate improved inference accuracy.

Real data application confirms practical effectiveness.

Abstract

The inflated beta regression model is widely used for modeling continuous proportions with values at the boundaries. Maximum likelihood estimation for these models is well-known for its sensitivity to outliers, which can severely distort inference and lead to misleading conclusions. We propose robust estimators that mitigate the lack of robustness in maximum likelihood-based inference while preserving the simplicity and interpretability of the inflated beta framework. Additionally, an algorithm is introduced to select tuning constants based on the data's robustness requirements. The proposed estimators' asymptotic and robustness properties are studied, and robust Wald-type tests are developed. Simulation studies and a real data application highlight the advantages and practical effectiveness of the proposed robust estimators.