7月9日 Jun Li( University of California) 学术报告
 
  发布时间: 2017-07-05  
 




学术报告


报告题目:Nonparametric Adaptive CUSUM Chart for Detecting Arbitrary Distributional Changes

报 告 人:Jun Li  Associate Professor

 Department of Statistics

 University of California, Riverside

时    间:79(星期日)上午 10:00

地    点:统计研究院426教室

摘    要: Nonparametric control charts that can detect arbitrary distributional changes are highly desirable due to their flexibility to adapt to different distributional assumptions and distributional changes. However, most of such control charts in the literature either involve some tuning parameter, which needs to be pre-specified, or involve intensive computation. In this talk, we propose a new nonparametric adaptive CUSUM chart for detecting arbitrary distributional changes. The proposed control chart does not depend on any tuning parameter and is efficient in computation. Its self-starting nature makes the proposed control chart applicable to situations where no sufficiently large reference data are available. Our proposed control chart also has a built-in post-signal diagnostics function that can identify what kind of distributional changes have occurred after an alarm. Our simulation study and real data analysis show that the proposed control chart performs well across a broad range of settings, and compares favorably with existing nonparametric control charts.

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