SEQUENTIAL CHANGE-POINT DETECTION WHEN THE PRE- AND POST-CHANGE PARAMETERS ARE UNKNOWN By
نویسندگان
چکیده
We describe asymptotically optimal Bayesian and frequentist solutions to the problem of sequential change-point detection in multiparameter exponential families when the pre-and post-change parameters are unknown. In this connection we also address certain issues recently raised by Mei (2008) concerning performance criteria for detection rules in this setting.
منابع مشابه
Sequential Change-Point Detection When the Pre- and Post-Change Parameters are Unknown
We describe asymptotically optimal Bayesian and frequentist solutions to the problem of sequential change-point detection in multiparameter exponential families when the preand post-change parameters are unknown. In this connection we also address certain issues recently raised by Mei (2008) concerning performance criteria for detection rules in this setting.
متن کاملSequential Change-point Detection When Unknown Parameters Are Present in the Pre-change Distribution 1
In the sequential change-point detection literature, most research specifies a required frequency of false alarms at a given pre-change distribution fθ and tries to minimize the detection delay for every possible post-change distribution gλ. In this paper, motivated by a number of practical examples, we first consider the reverse question by specifying a required detection delay at a given post...
متن کاملSEQUENTIAL CHANGE-POINT DETECTION WHEN UNKNOWN PARAMETERS ARE PRESENT IN THE PRE-CHANGE DISTRIBUTION1 BY YAJUN MEI California Institute of Technology and Fred Hutchinson Cancer Research Center
In the sequential change-point detection literature, most research specifies a required frequency of false alarms at a given pre-change distribution fθ and tries to minimize the detection delay for every possible post-change distribution gλ. In this paper, motivated by a number of practical examples, we first consider the reverse question by specifying a required detection delay at a given post...
متن کاملA BAYESIAN APPROACH TO SEQUENTIAL SURVEILLANCE IN EXPONENTIAL FAMILIES By
We describe herein a Bayesian change-point model and the associated recursive formulas for the estimated time-varying parameters and the posterior probability that a change-point has occurred at a particular time. The proposed model is a variant of that of Chernoff and Zacks (1964) for the case of normal means with known common variance. It considers more generally the multiparameter exponentia...
متن کاملAsymptotically Optimal Methods for Sequential Change-Point Detection
This thesis studies sequential change-point detection problems in different contexts. Our main results are as follows: • We present a new formulation of the problem of detecting a change of the parameter value in a one-parameter exponential family. Asymptotically optimal procedures are obtained. • We propose a new and useful definition of “asymptotically optimal to firstorder” procedures in cha...
متن کامل