Reliable detection of abrupt changes and a multi-parameter exponential distribution

Jeudi 21 novembre 2019, 10:15 à 11:15

Salle de séminaires, M.0.1

Igor Nikiforov

Institut Charles Delaunay, Université de Technologie de Troyes.

The goal of this presentation is to discuss the reliable sequential detection of transient changes in a multi-parameter exponential distribution. The sequentially observed data are represented by a sequence of independent random vectors with the exponentially distributed components. The parameter vector consists of the expected values of exponentially distributed random variables (components of the vectors).  This parameter vector changes at an unknown time (changepoint). It is necessary to reliably detect this changepoint.  The considered optimality criterion minimizes the worst-case probability of missed detection provided that the worst-case probability of false alarm during a certain period is upper bounded.  Special attention is paid to the problem of change detectability. The maximum/minimum contrast vectors of post-change parameters are defined w.r.t. the vector of pre-change parameters by using a quadratic maximization/minimization problem. An application of the obtained results to the detection of spectral changes is also considered.