基于连续优化的离线变点检测与估计
Continuous Optimization for Offline Change Point Detection and Estimation
AI总结：
本文将最佳子集选择的连续优化框架COMBSS适配于单变量高斯序列的离线变点检测，通过稀疏正则化逆问题建模，并在模拟数据上验证了不同惩罚参数选择策略的有效性。
摘要：
本文探索利用回归建模中最佳子集选择的最新连续优化进展，对单变量高斯数据序列进行离线变点检测与估计。该方法将正态均值多变点模型重构为一个强制稀疏性的正则化统计逆问题。在介绍问题陈述、准则以及此前基于Lasso正则化的研究之后，简要介绍了最近发展的最佳子集选择连续优化（COMBSS）框架，并将其与当前问题联系起来。本文探讨了有监督和无监督两种视角，其中无监督视角通过偏差原则和置信界测试了选择正则化惩罚参数的不同方法。主要结果是对COMBSS方法在离线正态均值多变点检测中的适配与评估，通过在模拟数据上对不同正则化参数选择进行实验验证。最后讨论了结果和未来研究方向。
英文摘要：
This work explores use of novel advances in best subset selection for regression modelling via continuous optimization for offline change point detection and estimation in univariate Gaussian data sequences. The approach exploits reformulating the normal mean multiple change point model into a regularized statistical inverse problem enforcing sparsity. After introducing the problem statement, criteria and previous investigations via Lasso-regularization, the recently developed framework of continuous optimization for best subset selection (COMBSS) is briefly introduced and related to the problem at hand. Supervised and unsupervised perspectives are explored with the latter testing different approaches for the choice of regularization penalty parameters via the discrepancy principle and a confidence bound. The main result is an adaptation and evaluation of the COMBSS approach for offline normal mean multiple change-point detection via experimental results on simulated data for different choices of regularisation parameters. Results and future directions are discussed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
