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

页码：161 / 179

页数：19

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