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Efficient Estimation for Structure Break Model
编辑:      澳门新葡新京:2017-11-20       点击数:
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报告题目:Efficient Estimation for Structure Break Model

报告人:浙江大学张荣茂(教授)

报告时间:2017年11月22日下午4:00

报告地点:澳门新葡新京201报告厅

摘要:Structure break (SBR) model has attracted considerable attention in diverse areas such as signal processing, biological sciences, econometrics, environmental sciences, finance, hydrology, physics and population dynamics. However, the estimation of SBR model constitutes a difficult task. In this talk, we will introduce a novel approach for estimating structure break (SBR) models with multiple-regime and discuss their large sample properties. By reframing the problem in a sparse variable selection context, the group least absolute shrinkage and selection operator (LASSO) is proposed to estimate an SBR model with an unknown number of break locations, where the computation can be performed efficiently. We show that the number of break points and the location of the break points can be consistently estimated. We also establish near optimal the convergence rate of the break points. An improved version that incorporates group LASSO and stepwise regression variable selection technique and its extension to spatial change will also be discussed. Simulation studies and real data analysis are conducted to illustrate the performance of the proposed method.

报告人概况:张荣茂博士,教授,博士生导师,浙江大学数学系统计研究所副所长,主要从事随机场的渐近理论和非平稳时间序列的研究,在国际重要SCI杂志发表论文30多篇。任《Journal of the Korean Statistical Society》等杂志编委。2004年在浙江大学获得博士学位,2004年7月-2006年6月在北京大学数学学院概率统计系从事博士后研究,2006年至今在浙江大学工作。多次访问香港科大、香港中文大学,2014年5月-2015年5月在伦敦政治经济学院访问。研究兴趣为:大样本统计理论、非线性金融时间序列、经验似然估计、非参数统计、空间数据分析。


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