Detecting change-points in multivariate settings is usually carried out by analyzing all marginals either independently, via univariate methods, or jointly, through approaches. The former discards any inherent dependencies between different and the latter may suffer from domination/masking among of distinct marginals. As a remedy, we propose an approach which groups with similar temporal behavi...

Many experiments record sequential trajectories where each trajectory consists of oscillations and fluctuations around zero. Such can be viewed as zero-mean functional data. When there are structural breaks (on the sequence trajectories) in higher order moments, it is not always easy to spot these by mere visual inspection. Motivated this challenging problem brain signal analysis, we propose a ...

Change-points in time series data are usually defined as the instants at which changes their properties occur. Detecting change-points is critical a number of applications diverse detecting credit card and insurance frauds, or intrusions into networks. Recently authors introduced an online kernel-based change-point detection method built upon direct estimation density ratio on consecutive inter...