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Kernel smoothing method for detecting fixed and random mean change in multivariate data
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AI
DOI：10.1080/07474946.2025.2594991.png)
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Abstract
En 中文
To enhance the effectiveness of sequential detection methods, we introduce a kernel smoothing moving average (KSMA) chart that places greater emphasis on recent observations. This new approach is benchmarked against established methods, including the finite moving average (MA), exponentially weighted moving average (EWMA), and CUSUM charts, evaluated in terms of the conditional average detection delay time (ADDT) for a given in-control average run length (ARL0) under persistent change. A tailored kernel function is proposed to maximize asymptotic efficiency across the signal strength spectrum while maintaining consistently high performance. The comparative study considers both fixed-mean and random-mean change models in multivariate settings. Our results indicate that for moderate signal strengths with fixed mean changes, the KSMA procedure outperforms the alternatives, whereas the EWMA procedure is superior when the signal strength is small. Overall, both the EWMA and KSMA procedures perform competitively and consistently surpass the other methods across fixed and random mean change scenarios. Applications to Dow Jones stock prices and EEG monitoring further illustrate the practical value of the proposed approach. Additional dimension reduction techniques for sparse signal detection are also briefly discussed.
Keywords:
Average delay detection time
average run length
exponentially weighted moving average
finite moving average
Kernel smoothing moving average
Journal
S
IF:
0.6
Papers:
23
Citations:
0
