Group LASSO for Change-points in Functional Time Series  

作　　者：Chang Xiong CHI Rong Mao ZHANG 

机构地区：[1]School of Mathematical Sciences,Zhejiang University,Hangzhou 310058,P.R.China

出　　处：《Acta Mathematica Sinica,English Series》2023年第11期2075-2090,共16页数学学报（英文版）

基　　金：NSFC(Grant No.12171427/U21A20426/11771390);Zhejiang Provincial Natural Science Foundation(Grant No.LZ21A010002);the Fundamental Research Funds for the Central Universities(Grant No.2021XZZX002)。

摘　　要：Multiple change-points estimation for functional time series is studied in this paper.The change-point problem is first transformed into a high-dimensional sparse estimation problem via basis functions.Group least absolute shrinkage and selection operator(LASSO)is then applied to estimate the number and the locations of possible change points.However,the group LASSO(GLASSO)always overestimate the true points.To circumvent this problem,a further Information Criterion(IC)is applied to eliminate the redundant estimated points.It is shown that the proposed two-step procedure estimates the number and the locations of the change-points consistently.Simulations and two temperature data examples are also provided to illustrate the finite sample performance of the proposed method.

关 键 词：Basis function CHANGE-POINT functional time series information criterion group LASSO(GLASSO) 

分 类 号：O212.1[理学—概率论与数理统计]