High-dimensional changepoint estimation via sparse projection

来源 :上海交通大学 | 被引量 : 0次 | 上传用户：macrosoft

【摘要】

：

【作者】

：

Richard J.Samworth Tengyao Wang

【机构】

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Statistical Laboratory,University of Cambridge,CB3 0WB,United Kingdom

【出处】

：

上海交通大学

【发表日期】

：

2016年4期

【关键词】

：

Changepoint CUSUM transformation high-dimensional time series sparse projection

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　　Changepoints are a very common feature of Big Data that arrive in the form of a data stream.In this paper,we study high-dimensional time series in which,at certain time points,the mean structure changes in a sparse subset of the coordinates.The challenge is to borrow strength across the coordinates in order to detect smaller changes than could be observed in any individual component series.

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High-dimensional changepoint estimation via sparse projection

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