Annals of Statistics
- Ann. Statist.
- Volume 33, Number 3 (2005), 1422-1454.
Nonanticipating estimation applied to sequential analysis and changepoint detection
Suppose a process yields independent observations whose distributions belong to a family parameterized by θ∈Θ. When the process is in control, the observations are i.i.d. with a known parameter value θ0. When the process is out of control, the parameter changes. We apply an idea of Robbins and Siegmund [Proc. Sixth Berkeley Symp. Math. Statist. Probab. 4 (1972) 37–41] to construct a class of sequential tests and detection schemes whereby the unknown post-change parameters are estimated. This approach is especially useful in situations where the parametric space is intricate and mixture-type rules are operationally or conceptually difficult to formulate. We exemplify our approach by applying it to the problem of detecting a change in the shape parameter of a Gamma distribution, in both a univariate and a multivariate setting.
Ann. Statist., Volume 33, Number 3 (2005), 1422-1454.
First available in Project Euclid: 1 July 2005
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Lorden, Gary; Pollak, Moshe. Nonanticipating estimation applied to sequential analysis and changepoint detection. Ann. Statist. 33 (2005), no. 3, 1422--1454. doi:10.1214/009053605000000183. https://projecteuclid.org/euclid.aos/1120224108