This monograph is the first to systematically study the bias of estimators and construction of corrected confidence intervals for change-point and post-change parameters after a change is detected by using a CUSUM procedure. Researchers in change-point problems and sequential analysis, time series and dynamic systems, and statistical quality control will find that the methods and techniques are mostly new and can be extended to more general dynamic models where the structural and distributional parameters are monitored. Practitioners, who are interested in applications to quality control, dynamic systems, financial markets, clinical trials and other areas, will benefit from case studies based on data sets from river flow, accident interval, stock prices, and global warming. Readers with an elementary probability and statistics background and some knowledge of CUSUM procedures will be able to understand most results as the material is relatively self-contained.

The main emphasis is on the inference problem for the change point and post-change parameters after a change has been detected

CUSUM Procedure.- Change-Point Estimation.- Confidence Interval for Change-Point.- Inference for Post-Change Mean.- Estimation After False Signal.- Inference with Change in Variance.- Sequential Classification and Segmentation.- An Adaptive CUSUM Procedure.- Dependent Observation Case.- Other Methods and Remarks.