Fabrizio Ghezzi, Eduardo Rossi, Lorenzo Trapani
arXiv 6 Feb 2024 · Statistics — Methodology · 1 citations (OpenAlex)
arXiv:2402.04433 · PDF · DOI · OpenAlex · Extracted main text
We study online changepoint detection in the context of a linear regression model. We propose a class of heavily weighted statistics based on the CUSUM process of the regression residuals, which are specifically designed to ensure timely detection of breaks occurring early on during the monitoring horizon. We subsequently propose a class of composite statistics, constructed using different weighing schemes; the decision rule to mark a changepoint is based on the largest statistic across the various weights, thus effectively working like a veto-based voting mechanism, which ensures fast detection irrespective of the location of the changepoint. Our theory is derived under a very general form of weak dependence, thus being able to apply our tests to virtually all time series encountered in economics, medicine, and other applied sciences. Monte Carlo simulations show that our methodologies are able to control the procedure-wise Type I Error, and have short detection delays in the presence of breaks.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Horváth, L., M. Husková, P. Kokoszka, and J. Steinebach (2004) Monitoring changes in linear models | 1.000 | 8 | 5 | 100% |
| 2 | Berkes, I., S. Hörmann, and J. Schauer (2011) Split invariance principles for stationary processes | 1.000 | 6 | 3 | 100% |
| 3 | Romano, G., I. A. Eckley, P. Fearnhead, and G. Rigaill (2023) Fast online changepoint detection via functional pruning CUSUM statistics | 0.874 | 5 | 2 | 100% |
| 4 | Horváth, L., P. Kokoszka, and J. Steinebach (2007) On sequential detection of parameter changes in linear regression | 0.843 | 3 | 3 | 100% |
| 5 | Horváth, L. and L. Trapani (2023) Real-time monitoring with RCA models | 0.811 | 4 | 2 | 100% |
| 6 | Aue, A. and L. Horváth (2004) Delay time in sequential detection of change | 0.811 | 4 | 2 | 100% |
| 7 | Kirch, C. and S. Weber (2018) Modified sequential change point procedures based on estimating functions | 0.737 | 3 | 2 | 100% |
| 8 | Yu, Y., O. H. M. Padilla, D. Wang, and A. Rinaldo (2020) A note on online change point detection | 0.737 | 3 | 2 | 100% |
| 9 | Aue, A., L. Horváth, P. Kokoszka, and J. Steinebach (2008) Monitoring shifts in mean: asymptotic normality of stopping times | 0.644 | 2 | 2 | 100% |
| 10 | Kirch, C. and C. Stoehr (2022) Sequential change point tests based on U-statistics | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 38 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Sequential monitoring for explosive volatility regimes | 1.000 | 5 | 3 |