The Impact of Statistical Model Averaging on Predictive Accuracy in Complex Data Environments

Posted: Nov 30, 2018

• Adrian Price
• Alexa Brooks
• Alexander Bennett

Abstract

This research investigates the efficacy of statistical model averaging techniques in enhancing predictive accuracy within complex data environments characterized by high dimensionality, non-stationarity, and heterogeneous data structures. Traditional model selection approaches often suffer from instability and overconfidence in single-model predictions, particularly when faced with the inherent uncertainty present in complex datasets. We propose a novel framework called Adaptive Bayesian Model Averaging (ABMA) that dynamically weights competing statistical models based on their evolving predictive performance across different regions of the feature space. Unlike conventional model averaging methods that apply static weights, ABMA incorporates spatial and temporal adaptation mechanisms that respond to local data characteristics and distribution shifts. Our methodology integrates elements from Bayesian statistics, ensemble learning, and online learning to create a robust predictive system that acknowledges model uncertainty as a fundamental component of the inference process. Through extensive experimentation on synthetic and real-world datasets spanning financial markets, ecological monitoring, and healthcare analytics, we demonstrate that ABMA consistently outperforms both individual models and traditional averaging approaches by 15-28