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This study introduces a new method for change-point detection in time series, specifically for detecting mean shifts in weakly dependent data. The proposed empirical likelihood approach offers advantages over classical methods for real-world datasets.

Keywords:
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Area of Science:

  • Statistics
  • Time Series Analysis

Background:

  • Change-point detection identifies structural breaks in data.
  • Weakly dependent data often violates assumptions of standard statistical tests.
  • Detecting mean shifts is crucial for analyzing real-world time series.

Purpose of the Study:

  • To propose a novel method for detecting mean shifts in weakly dependent time series.
  • To introduce adjusted p-value graphs for graphical and numerical change-point localization.
  • To compare the proposed method with existing techniques via simulation and real-world data.

Main Methods:

  • Utilizing the two-sample blockwise empirical likelihood for the difference of two-sample means.
  • Developing adjusted p-value graphs for significance and location detection.
  • Conducting simulation studies and applying the method to historical wind speed data.

Main Results:

  • The proposed empirical likelihood method demonstrates advantages for weakly dependent observations.
  • Adjusted p-value graphs effectively locate change-points both graphically and numerically.
  • The method shows applicability to real-world datasets, exemplified by wind speed analysis.

Conclusions:

  • The two-sample blockwise empirical likelihood is a robust method for change-point detection in weakly dependent data.
  • The R-package 'EL' facilitates the implementation of this novel approach.
  • This method enhances the analysis of time series data with complex dependence structures.