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| 베이즈 회귀× | 시계열 단절 분석 (Interrupted Time Series, ITS)× | |
|---|---|---|
| 분야≠ | 베이지안 | 인과추론 |
| 계열≠ | Bayesian methods | Regression model |
| 기원 연도≠ | — | 2002 |
| 창시자≠ | — | Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial) |
| 유형≠ | Bayesian linear model | Quasi-experimental segmented regression |
| 원전≠ | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 | Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355. DOI ↗ |
| 별칭 | bayesian linear regression, probabilistic regression, bayesian regresyon | ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi |
| 관련≠ | 2 | 5 |
| 요약≠ | Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off. | Interrupted Time Series analysis is a quasi-experimental design that estimates the effect of a single, well-dated intervention by comparing the trajectory of an outcome before and after it occurs. Formalised as segmented regression by Wagner and colleagues (2002) and popularised as a public-health evaluation tutorial by Bernal, Cummins and Gasparrini (2017), it separates the intervention's impact into a change in level and a change in slope. |
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