Detecting the emergence of an abrupt change-point is a classic problem in statistics and machine learning. Kernel-based nonparametric statistics have been proposed for this task which make fewer assumptions on the distributions than traditional parametric approach. However, none of the existing kernel statistics has provided a computationally efficient way to characterize the extremal behavior ...

The contribution presents to analyses and comparison of the recursive (sliding window) Bayesian autoregressive normalized change-point detector (RBACDN) and the reversible jump Markov chain Monte Carlo method (RJMCMC) when they are used for the localization of signal changes (change-point detection). The choice of priors and parameter setting for the RJMCMC and the RBACDN are discussed. The eva...

For the problem of sequential change detection we propose a novel modelling of the change-point mechanism. In particular we regard the time of change as a stopping time controlled by Nature. Nature, in order to decide when to impose the change, accesses sequentially information which can be different from the information provided to the Statistician to detect the change. Using as performance me...

the major aim of processing satellite images is to prepare topical and effectivemaps. the selection of appropriate classification methods plays an important role. amongvarious methods existing for image classification, artificial neural network method is ofhigh accuracy. in present study, tm images of 1987, and etm+ images of 2000 and 2006were analyzed using artificial fuzzy artmap neural netwo...

We consider an epidemic change-point detection in a large class of causal time series models, including among other processes, AR( ∞ ), ARCH( TARCH( ARMA-GARCH. A test statistic based on the Gaussian quasi-maximum likelihood estimator parameter is proposed. It shown that, under null hypothesis no change, converges to distribution obtained from difference two Brownian bridge and diverges infinit...