M-procedures robust to structural changes detection under strong mixing heavy-tailed time series models

Many tests of change points resort to least squares estimation method, but it can lead to bias if these observations are heavy-tailed processes. The aim of this paper is to construct a ratio-typed test based on M-estimation, which avoids the long-range variance estimation and is robust to structural change detection under strong mixing series with heavy-tailed. The proposed test consisting of M-procedures has more utility in that it allows processes in the domain of attraction of a stable law with index κ ∈ ( 0 , 2 ) , not limited to ( 1 , 2 ) . Under some regular conditions, asymptotic distribution under the null hypothesis of no change is functional of a Brownian motion, and the divergent rate under the alternative hypothesis is also provided. Furthermore, the convergence rate of a ratio-typed change point estimator is established. Simulation study illustrates there is no distortion in empirical sizes, and empirical powers have satisfactory performance. Finally, two practical applications to real examples are presented as well. • Proposed a ratio-typed M-estimation statistic free from long-range variance estimation. • Heavy-tailed index is extended from the range (1,2) to (0,2), allowing mean-absent cases. • Numerical simulations verify our robust change-point tests for heavy-tailed data.