A Test for Detecting Changes in Mean

  • Wei Biao Wu
Conference paper
Part of the The IMA Volumes in Mathematics and its Applications book series (IMA, volume 139)


In the classical time series analysis, a process is often modeled as three additive components: long-time trend, seasonal effect and background noise. Then the trend superimposed with the seasonal effect constitute the mean part of the process. The issue of mean stationarity, which is generically called change-point problem, is usually the first step for further statistical inference. In this paper we develop testing theory for the existence of a long-time trend. Applications to the global temperature data and the Darwin sea level pressure data are discussed. Our results extend and generalize previous ones by allowing dependence and general patterns of trends.


Seasonal Effect Seasonal Component Isotonic Regression Uniformly Much Powerful Monthly Temperature Data 
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Copyright information

© Springer-Verlag New York, LLC 2004

Authors and Affiliations

  • Wei Biao Wu
    • 1
  1. 1.Department of StatisticsUniversity of ChicagoChicagoUSA

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