Consistent two-stage multiple change-point detection in linear models

被引：14
|
作者：
Jin, Baisuo [1 ]
Wu, Yuehua [2 ]
Shi, Xiaoping [3 ]
机构：
[1] Univ Sci & Technol China, Dept Stat & Finance, 96 Jinzhai Rd, Hefei 230026, Anhui, Peoples R China
[2] York Univ, Dept Math & Stat, 4700 Keele St, Toronto, ON M3J 1P3, Canada
[3] Thompson Rivers Univ, Dept Math & Stat, 900 McGill Rd, Kamloops, BC V2C 0C8, Canada
来源：
CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE | 2016年 / 44卷 / 02期
基金：
加拿大自然科学与工程研究理事会; 中国国家自然科学基金;
关键词：
Adaptive lasso; consistency; MCP/SCAD; model selection; multiple change-point detection; STRUCTURAL BREAK ESTIMATION; TIME-SERIES MODELS; VARIABLE SELECTION; BAYESIAN-ANALYSIS; ORACLE PROPERTIES; ADAPTIVE LASSO; SHRINKAGE; PENALTY; SQUARES; ORDER;
D O I：
10.1002/cjs.11282
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
A two-stage procedure for simultaneously detecting multiple change-points in linear models is developed. In the cutting stage, the change-point problem is converted into a model selection problem so that a modern model selection method can be applied. In the refining stage, the change-points obtained in the cutting stage are finalized via a refining method. Under mild conditions, consistency of the number of change-point estimates is established. The new procedure is fast and accurate, as shown in simulation studies. Its applicability in real situations is demonstrated via well-log and ozone data. (C) 2016 Statistical Society of Canada
引用
收藏
页码：161 / 179
页数：19
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