Browsing by Subject "Wiener disorder problem"

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Open Access
Compound Poisson disorder with general prior and misspecified Wiener disorder problem
(2024-07) Şahin, Deniz
For a system modeled with a compound Poisson or a Wiener process, let us assume that the underlying model parameters change at an unknown and unobservable time. For a compound Poisson process, these are arrival rate and mark distribution while for a Wiener process, it is the drift parameter. Suppose the decision maker knows the pre- and post-disorder process parameters, as well as the prior density of the disorder time. In this case, finding a stopping rule that optimizes a Bayesian penalty function is called the compound Poisson and Wiener disorder problem, respectively. For the compound Poisson problem, we consider a general prior distribution where the decision maker has more general knowledge about the disorder time than exponential and uniform priors which were addressed in the previous studies. For the Wiener problem, we revisit the asset selling problem with an exponential prior, where the decision maker specifies problem parameters incorrectly. In both cases, the original problems reduce to optimal stopping problems. We use time discretization and successive approximation methods for the first case and Markov chain approximation and Monte Carlo simulations for the second case. We provide the quickest detection rules and discuss various numerical examples.
Open Access
Sequential sensor installation for wiener disorder detection
(Institute for Operations Research and the Management Sciences (I N F O R M S), 2016) Dayanik, S.; Sezer, S. O.
We consider a centralized multisensor online quickest disorder detection problem where the observation from each sensor is a Wiener process gaining a constant drift at a common unobservable disorder time. The objective is to detect the disorder time as quickly as possible with small probability of false alarms. Unlike the earlier work on multisensor change detection problems, we assume that the observer can apply a sequential sensor installation policy. At any time before a disorder alarm is raised, the observer can install new sensors to collect additional signals. The sensors are statistically identical, and there is a fixed installation cost per sensor. We propose a Bayesian formulation of the problem. We identify an optimal policy consisting of a sequential sensor installation strategy and an alarm time, which minimize a linear Bayes risk of detection delay, false alarm, and new sensor installations. We also provide a numerical algorithm and illustrate it on examples. Our numerical examples show that significant reduction in the Bayes risk can be attained compared to the case where we apply a static sensor policy only. In some examples, the optimal sequential sensor installation policy starts with 30% less number of sensors than the optimal static sensor installation policy and the total percentage savings reach to 12%.
Open Access
Wiener disorder problem with observations at fixed discrete time epochs
(Institute for Operations Research and the Management Sciences (I N F O R M S), 2010) Dayanik, S.
Suppose that a Wiener process gains a known drift rate at some unobservable disorder time with some zero-modified exponential distribution. The process is observed only at known fixed discrete time epochs, which may not always be spaced in equal distances. The problem is to detect the disorder time as quickly as possible by means of an alarm that depends only on the observations of Wiener process at those discrete time epochs. We show that Bayes optimal alarm times, which minimize expected total cost of frequent false alarms and detection delay time, always exist. Optimal alarms may in general sound between observation times and when the space-time process of the odds that disorder happened in the past hits a set with a nontrivial boundary. The optimal stopping boundary is piecewise-continuous and explodes as time approaches from left to each observation time. On each observation interval, if the boundary is not strictly increasing everywhere, then it irst decreases and then increases. It is strictly monotone wherever it does not vanish. Its decreasing portion always coincides with some explicit function. We develop numerical algorithms to calculate nearly optimal detection algorithms and their Bayes risks, and we illustrate their use on numerical examples. The solution of Wiener disorder problem with discretely spaced observation times will help reduce risks and costs associated with disease outbreak and production quality control, where the observations are often collected and/or inspected periodically.