Description

The concept of homogeneity plays a critical role in statistics, both in its applications as well as its theory. Change point analysis is a statistical tool that aims to attain homogeneity within time series data. This is accomplished through partitioning the time series into a number of contiguous homogeneous segments. The applications of such techniques range from identifying chromosome alterations to identifying economic recessions to solar flare detection.

Our work in this area focuses on nonparametric estimation of both the number of change points and the positions at which they occur.

Related Publications

Preprints

James, N.A. and Matteson, D.S. (2015), “Change Points via Probabilistically Pruned Objectives.”

James, N.A., Kejariwal, A. and Matteson, D.S. (2015), “Leveraging Cloud Data to Mitigate User Experience from Breaking Bad: The Twitter Approach.”

Articles

James, N.A. and Matteson, D.S. (2015), “ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data,” Journal of Statistical Software, Vol. 62, No. 7: 1-25.

Matteson, D.S. and James, N.A. (2014), “A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data,” Journal of the American Statistical Association, Vol. 109, No. 505, 334-345.

Holan, S.H., Yang, W.-H., Matteson, D.S. and Wikle, C.K. (2012), “An Approach for Identifying and Predicting Economic Recessions in Real-Time Using Time-Frequency Functional Models,” Applied Stochastic Models in Business and Industry, Vol. 28, No. 6, 485-499.

Holan, S.H., Yang, W.-H., Matteson, D.S. and Wikle, C.K. (2012), “Rejoinder, An Approach for Identifying and Predicting Economic Recessions in Real-Time Using Time-Frequency Functional Models,” Applied Stochastic Models in Business and Industry, Vol. 28, No. 6, 504-505.

Code

• R Package: ecp – Nonparametric Multiple Change Point Analysis of Multivariate Data (2013)
  • James, N.A. and Matteson, D.S.

Research Projects