Sequential change point detection for high‐dimensional data using nonconvex penalized quantile regression-Reference-Cited by-同舟云学术

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Sequential change point detection for high‐dimensional data using nonconvex penalized quantile regression

Published:2020-11-16 Issue:3 Volume:63 Page:575-598
ISSN:0323-3847
Container-title:Biometrical Journal
language:en
Short-container-title:Biometrical Journal

Author:

Ratnasingam Suthakaran¹,Ning Wei²³^ORCID

Affiliation:

1. Department of Mathematics California State University San Bernardino CA USA

2. Department of Mathematics and Statistics Bowling Green State University Bowling Green OH USA

3. School of Mathematics and Statistics Beijing Institute of Technology Beijing P. R. China

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,General Medicine,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/bimj.202000078

Reference38 articles.

1. A new look at the statistical model identification

2. Segmented Model Selection in Quantile Regression Using the Minimum Description Length Principle

3. Testing for Parameter Constancy in Linear Regressions: An Empirical Distribution Function Approach

4. Least squares after model selection in high-dimensional sparse models

5. Variable selection in high-dimensional linear models: partially faithful distributions and the PC-simple algorithm

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Nonparametric Shiryaev-Roberts change-point detection procedures based on modified empirical likelihood;Journal of Applied Statistics;2024-01-23

2. Real-time change point detection in linear models using the ranking selection procedure;Sequential Analysis;2023-04-03

3. Monitoring sequential structural changes in penalized high-dimensional linear models;Sequential Analysis;2021-07-03

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