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• W2884455973 abstract "The paper addresses a sequential changepoint detection problem for a general stochastic model, assuming that the observed data may be (i.e., dependent and non-identically distributed) and the prior distribution of the change point is arbitrary. Tartakovsky and Veeravalli (2005), Baron and Tartakovsky (2006), and, more recently, Tartakovsky (2017) developed a general asymptotic theory of changepoint detection for non-i.i.d. stochastic models, assuming the certain stability of the log-likelihood ratio process, in the case of simple hypotheses when both pre-change and post-change models are completely specified. However, in most applications, the post-change distribution is not completely known. In the present paper, we generalize previous results to the case of parametric uncertainty, assuming the parameter of the post-change distribution is unknown. We introduce two detection rules based on mixtures -- the Mixture Shiryaev rule and the Mixture Shiryaev--Roberts rule -- and study their asymptotic properties in the Bayesian context. In particular, we provide sufficient conditions under which these rules are first-order asymptotically optimal, minimizing moments of the delay to detection as the probability of false alarm approaches zero." @default.
• W2884455973 created "2018-08-03" @default.
• W2884455973 creator A5000541084 @default.
• W2884455973 date "2018-07-24" @default.
• W2884455973 modified "2023-09-27" @default.
• W2884455973 title "Asymptotic Optimality of Mixture Rules for Detecting Changes in General Stochastic Models" @default.
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• W2884455973 hasPublicationYear "2018" @default.
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