Simultaneous change-point detection and curve estimation

被引:0
|
作者
Lu, Zhaoying [1 ]
Hao, Ning [1 ]
Zhang, Hao Helen [1 ]
机构
[1] Univ Arizona, Dept Math, Tucson, AZ 85721 USA
基金
美国国家科学基金会; 美国国家卫生研究院;
关键词
B-splines; Jump regression; Nonparametric regression; Normalized fused Lasso; BINARY SEGMENTATION; JUMP-DETECTION; REGRESSION; NUMBER; INFORMATION; ALGORITHM; SELECTION; MODELS;
D O I
暂无
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
In this work, we focus on a nonparametric regression model that accounts for discontinuities. We propose a method called Simultaneous CHange-point detection And Curve Estimation (SCHACE) for effectively detecting jumps in a data sequence and accurately capturing nonlinear trends between these jumps in the mean curve. The SCHACE is a unified regularization framework that incorporates two statistical tools: the normalized fused Lasso for change-point detection and B-splines for curve estimation. Notably, this approach is a single-step method that does not require iteration and is straightforward to implement. We demonstrate the advantages of the SCHACE by simulated and real-world data examples.
引用
收藏
页码:493 / 500
页数:8
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