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- W4364356961 abstract "This study considers the change-point test problem for time series based on the self-normalization ratio statistic test, which is constructed using residuals obtained from a support vector regression (SVR)-autoregressive moving average (ARMA) model. Under the null hypothesis, the series is a stationary process, and our test statistic converges to a non-degenerate distribution. Under the alternative hypothesis, there are change-points in the time series, and the self-normalization test statistic diverges to infinity. The simulations show that our proposed new test has better finite sample performance than other SVR-based tests in the literature. Finally, we illustrate its usefulness by analyzing two actual data sets." @default.
- W4364356961 created "2023-04-12" @default.
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- W4364356961 date "2023-04-11" @default.
- W4364356961 modified "2023-09-27" @default.
- W4364356961 title "Structural Change-Point Detection for Time Series via Support Vector Regression and Self-Normalization Method" @default.
- W4364356961 doi "https://doi.org/10.20944/preprints202304.0217.v1" @default.
- W4364356961 hasPublicationYear "2023" @default.
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