方法对比
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| 中断时间序列（ITS）分析× | 马尔可夫链蒙特卡洛 (MCMC)× | |
|---|---|---|
| 领域≠ | 因果推断 | 贝叶斯 |
| 方法族≠ | Regression model | Bayesian methods |
| 起源年份≠ | 2002 | — |
| 提出者≠ | Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial) | — |
| 类型≠ | Quasi-experimental segmented regression | Posterior sampling algorithm |
| 开创性文献≠ | 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 ↗ | 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 |
| 别名 | ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) |
| 相关≠ | 5 | 3 |
| 摘要≠ | 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. | Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model. |
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