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| 经验贝叶斯× | 混合效应模型× | |
|---|---|---|
| 领域≠ | 贝叶斯 | 统计学 |
| 方法族≠ | Bayesian methods | Regression model |
| 起源年份≠ | — | 1982 |
| 提出者≠ | Herbert Robbins (1956); Bradley Efron & Carl Morris (1973) | Laird & Ware |
| 类型≠ | Empirical Bayes estimator | Mixed effects regression |
| 开创性文献≠ | Robbins, H. (1956). An empirical Bayes approach to statistics. In J. Neyman (Ed.), Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1 (pp. 157–164). University of California Press. DOI ↗ | Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974. DOI ↗ |
| 别名≠ | EB, empirical Bayes estimation, marginal likelihood estimation, James-Stein shrinkage | LME, LMM, mixed model, random effects model |
| 相关 | 4 | 4 |
| 摘要≠ | Empirical Bayes (EB) is an estimation strategy, introduced by Herbert Robbins in 1956 and developed into practical shrinkage estimators by Bradley Efron and Carl Morris in 1973, in which the hyperparameters of the prior distribution are estimated from the observed data via the marginal likelihood rather than specified in advance. The resulting posterior retains a Bayesian structure but substitutes data-driven hyperparameters for subjective ones, bridging frequentist shrinkage and full Bayesian inference. | A mixed effects model (or linear mixed model) extends ordinary regression by including both fixed effects — population-level parameters shared by all observations — and random effects that capture subject-, group-, or cluster-level variability. It is the standard tool for repeated-measures, longitudinal, and multilevel data where observations within the same unit are correlated. |
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