Dynamic Sampling Procedures for Detecting a Change in the Drift of Brownian Motion: A Non-Bayesian Model

David Assaf · Ya’acov Ritov

17 citations0/0 files storedThe Annals of Statistics

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Advanced Statistical Process Monitoring Probability and Statistical Research Fault Detection and Control Systems

Abstract

We consider dynamic procedures for sampling from a process (a Brownian motion) and stopping it after a change is detected. The basic idea is to conduct a sequence of similar SPRT's, each one of them done in negligible time, while not sampling at all between them. The procedures detect the change point much faster than the standard procedures with the same sampling rate and time to false alarm, but hold the sampling rate constant.

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1996Dynamic sampling policy for detecting a change in distribution, with a probability bound on false alarm

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