Likelihood-based statistics for enhancing inference and prediction in inverse gamma regression models

Abstract

This article develops two adjusted likelihood ratio statistics to improve small-sample inference in the reparameterized inverse gamma regression model. It also proposes uniform shrinkage factors based on these statistics to improve predictive performance. The methods are evaluated through Monte Carlo simulations and empirical applications.