Detection of multiple change-points in multivariate data
Abbreviated Journal Title
J. Appl. Stat.
regression trees; binary splitting; principle of optimality; separability; dynamic programming; DNA-SEQUENCE SEGMENTATION; TIME-SERIES; MODELS; PARAMETERS; Statistics & Probability
The statistical analysis of change-point detection and estimation has received much attention recently. A time point such that observations follow a certain statistical distribution up to that point and a different distribution - commonly of the same functional form but different parameters after that point - is called a change-point. Multiple change-point problems arise when we have more than one change-point. This paper develops a method for multivariate normally distributed data to detect change-points and estimate within-segment parameters using maximum likelihood estimation.
Journal of Applied Statistics
"Detection of multiple change-points in multivariate data" (2013). Faculty Bibliography 2010s. 4358.