Inference for multiple change points in heavy-tailed time series via rank likelihood ratio scan statistics

摘要

This paper proposes a rank likelihood ratio scan method for estimating multiple change points in piecewise heavy-tailed time series. It can effectively improve the estimate accuracy and solve the problem that the likelihood ratio scan method overestimates the change points in such a time series. A simulation and analyses of two sets of real data illustrate the efficiency of the method. (C) 2019 Elsevier B.V. All rights reserved.