A1074
Title: Nonparametric Shiryaev-Roberts change-point detection
Authors: Wei Ning - Bowling Green State University (United States) [presenting]
Abstract: Sequential change-point analysis, which identifies a change of probability distribution in an infinite sequence of random observations, has important applications in many fields. A good method should detect a change point as soon as possible and keep a low amount of false alarms. As one of the most popular methods, the Shiryaev-Roberts (SR) procedure holds many optimalities. However, its implementation requires the pre-change and post-change distributions to be known, which is not achievable in practice. A nonparametric version of the SR procedure is constructed by embedding different versions of empirical likelihood, assuming two training samples, before and after the change, are available for parameter estimations. Simulations are conducted to compare the performance of the proposed method with that of existing methods. The results show that when the underlying distribution is unknown, and training sample sizes are small, the proposed modified procedure shows an advantage by reducing the delay of detection.
