Analyzing the Role of Sequential Testing Procedures in Dynamic and Real-Time Statistical Decision-Making

Posted: Oct 08, 2024

• Gianna Foster
• Grace Brooks
• Hailey Butler

Abstract

Sequential testing procedures have long been recognized as powerful tools in statistical decision theory, yet their application in dynamic, real-time environments remains largely unexplored. Traditional sequential analysis, pioneered by Wald in the 1940s, primarily addresses scenarios with independent, identically distributed observations and predetermined stopping boundaries. However, modern applications in autonomous systems, financial markets, and healthcare monitoring demand statistical methods that can adapt to rapidly changing conditions, incorporate contextual information, and make decisions with minimal latency. This research addresses the fundamental gap between classical sequential testing theory and the requirements of contemporary real-time decision systems. The conventional approach to sequential testing assumes stationary data generating processes and fixed decision boundaries, which often leads to suboptimal performance in dynamic environments. When data streams exhibit time-varying characteristics or when decision contexts evolve rapidly, traditional methods may either fail to detect meaningful changes or generate excessive false positives. Our work introduces a novel framework that overcomes these limitations by integrating adaptive learning mechanisms with sequential decision procedures. This integration enables the system to continuously update its testing parameters based on incoming data and environmental feedback, resulting in improved decision accuracy and resource efficiency.