Theory of Estimation in Queueing Network: DE

This book presents a comprehensive study of estimation methods for two-stage open queueing networks, with and without feedback. It develops practical, distribution-free approaches to estimate traffic intensities, intensity vectors, and mean response times using consistent estimators and multiple bootstrap techniques, including standard, percentile, bias-corrected, accelerated, Bayesian, and bootstrap-t methods. Through detailed simulation studies, the book evaluates coverage accuracy, interval length, and efficiency of each method, highlighting how calibration significantly improves performance for small samples. The proposed techniques are applied across a variety of queueing models relevant to computer, communication, and service systems. By combining theoretical development with empirical validation, the book offers robust statistical tools for analyzing congestion, predicting system behavior, and guiding performance evaluation in complex real-world networks.