A0840
Title: Change-point detection in generalized extreme value distribution via generalized fiducial inference
Authors: Xia Cai - Hebei University of Science and Technology (China) [presenting]
Abstract: Generalized extreme value (GEV) distribution is used to analyze the maximum from a block of data. It is very useful to describe the unusual event rather than the usual event. A change-point detection procedure for GEV distribution based on generalized fiducial inference is proposed. The fiducial distribution of the change-point location is constructed. Meanwhile, Markov Chain Monte Carlo method combined with Gibbs sampling and the Metropolis-Hastings algorithm is utilized to estimate the location of the change point and its confidence interval. Simulation results show that the proposed generalized fiducial method performs better in accuracy, robustness and the length of confidence intervals. Finally, the proposed method is applied to the annual maximum rainfall data in Beijing.
