報(bào)告題目：Multiple Change Points Detection in High-DimensionalMultivariate Regression

報(bào)告時(shí)間：2021年6月30日 9：00——11：00

報(bào)告方式：寧夏大學(xué)寧遠(yuǎn)樓213（線(xiàn)上同步騰訊會(huì)議ID 305 6047 5990）

報(bào)告內(nèi)容：We consider the problem of detecting structural changes in a high-dimensional regression setting. The structural parameters are subject to abrupt changes of unknown magnitudes at unknown locations. We propose a new procedure that minimizes a penalized least-squares loss function via a dynamic programming algorithm for estimating the locations of change points. To alleviate the computational burden, we adopt a prescreening procedure by eliminating a large number of irrelevant points before implementing estimation procedure. The number of change points is determined via Schwarz’s information criterion. Under mild assumptions, we establish the consistency of the proposed estimators, and further provide error bounds for estimated parameters which achieve almost-optimal rate. Simulation studies show that the proposed method performs reasonably well in terms of estimation accuracy, and a real data example is used for illustration.

嘉賓簡(jiǎn)介：

馬曉燕，本科和碩士分別畢業(yè)于寧夏大學(xué)和武漢大學(xué)，現(xiàn)為南開(kāi)大學(xué)統(tǒng)計(jì)與數(shù)據(jù)科學(xué)院博士生。主要研究方向是高維數(shù)據(jù)統(tǒng)計(jì)推斷、變點(diǎn)和異常值檢測(cè)。

寧夏大學(xué)數(shù)學(xué)統(tǒng)計(jì)學(xué)院

寧夏師范學(xué)院數(shù)學(xué)與計(jì)算機(jī)科學(xué)學(xué)院

2021年6月28日