Change points detection in time series is an important area of research in statistics, has a long history and has many applications. However, very often change point analysis is only focused on the changes in the mean value of some quantity in a process. In this work we consider time series with discrete point changes which may contain a finite number of changes of probability density functions...

Change-points detection is one of important problems in data analysis. Traditional change-points detection method is based on exact data sets which can’t reflect prior information of data. In this paper, a new concept, called “fuzzy point data” which is defined by giving a fuzzy membership to the data in exact data sets, is proposed for helping us handle the confidence of data. We introduce reg...

This chapter considers the multiple change–point problem for time series, including strongly dependent processes, with an unknown number of change– points. We propose an adaptive method for finding the segmentation, i.e., the sequence of change–points τ with the optimal level of resolution. This optimal segmentation τ̂ is obtained by minimizing a penalized contrast function J(τ , y)+βpen(τ ). Fo...

We consider two problems concerning locating change points in a linear regression model. One involves jump discontinuities (change-point) in a regression model and the other involves regression lines connected at unknown points. We compare four methods for estimating single or multiple change points in a regression model, when both the error variance and regression coefficients change simultane...

This paper presents an attempt to continuously annotate the emotion and status of multimodal corpora for understanding psychotherapeutic interviews. The collected continuous annotations are then used as the signal data to find change points in the dialogues. Our target dialogues are carried between clients with some psychological problems and their therapists. We measured two values, namely the...

We observe n sequences at each of m sites and assume that they have evolved from an ancestral sequence that forms the root of a binary tree of known topology and branch lengths, but the sequence states at internal nodes are unknown. The topology of the tree and branch lengths are the same for all sites, but the parameters of the evolutionary model can vary over sites. We assume a piecewise cons...

Identifying the mutational processes that shape nucleotide composition of mitochondrial genome (mtDNA) is fundamental to better understand how these genomes evolve. Several methods have been proposed analyze DNA sequence and skewness, but most them lack any measurement statistical support or were not developed taking into account specificities genomes. A new methodology presented, which specifi...