Sequential change-point detection with likelihood ratios

We consider the problem of sequential change-point detection when the family of distributions is exponential, and distinguish between parameters of interest, and nuisance parameters. Likelihood ratios are used as test statistics, and their large sample approximations under the alternative hypothesis of change are given. Our formulae allow type II error approximations and they suggest different schemes for change detection and change-point estimation.