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| 베이지안 공분산 분석 (Bayesian ANCOVA)× | 베이지안 다변량 분산 분석 (Bayesian MANOVA)× | |
|---|---|---|
| 분야 | 통계학 | 통계학 |
| 계열 | Hypothesis test | Hypothesis test |
| 기원 연도≠ | 2012 (formalized; Bayesian general linear models since 1960s) | 1970s–2010s |
| 창시자≠ | Building on Jeffreys (1961) and developed formally for regression/ANCOVA by Rouder & Morey (2012) | Bayesian framework applied to MANOVA; foundational multivariate Bayesian work by Dickey (1974) and Rouder et al. (2012) |
| 유형≠ | Bayesian parametric covariate-adjusted group comparison | Bayesian multivariate group comparison |
| 원전≠ | Rouder, J. N., & Morey, R. D. (2012). Default Bayes factors for model selection in regression. Multivariate Behavioral Research, 47(6), 877–903. DOI ↗ | Olkin, I., & Rubin, H. (1964). Multivariate beta distributions and independence properties of the Wishart distribution. The Annals of Mathematical Statistics, 35(1), 261–269. DOI ↗ |
| 별칭 | Bayesian ANCOVA, Bayesian analysis of covariance, B-ANCOVA, Bayesian covariate-adjusted group comparison | Bayesian MANOVA, Bayesian multivariate ANOVA, BF-MANOVA, Bayesian multivariate group comparison |
| 관련 | 5 | 5 |
| 요약≠ | Bayesian Analysis of Covariance (Bayesian ANCOVA) extends classical ANCOVA by placing prior distributions on group effects and covariate slopes, then updating them with observed data to obtain posterior distributions and Bayes factors. It quantifies evidence for group differences on a continuous outcome after statistically adjusting for one or more continuous covariates, without relying on p-value thresholds. | Bayesian Multivariate Analysis of Variance (Bayesian MANOVA) extends the classical MANOVA framework by replacing null-hypothesis significance testing with Bayesian inference. It uses prior distributions on multivariate group means and covariance structures, updates them with data to yield posterior distributions, and quantifies evidence through Bayes factors rather than p-values. |
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