Сравнение методов

	Байесовский MANOVA	Множественная множественная регрессия
Область	Статистика	Статистика
Семейство	Hypothesis test	Regression model
Год появления	1970s–2010s	2007
Автор метода	Bayesian framework applied to MANOVA; foundational multivariate Bayesian work by Dickey (1974) and Rouder et al. (2012)	Johnson & Wichern (textbook treatment); classical multivariate least squares
Тип	Bayesian multivariate group comparison	Multivariate linear regression
Основополагающий источник	Olkin, I., & Rubin, H. (1964). Multivariate beta distributions and independence properties of the Wishart distribution. The Annals of Mathematical Statistics, 35(1), 261–269.	Johnson, R. A. & Wichern, D. W. (2007). Applied Multivariate Statistical Analysis (6th ed.). Pearson. ISBN: 978-0131877153
Другие названия	Bayesian MANOVA, Bayesian multivariate ANOVA, BF-MANOVA, Bayesian multivariate group comparison	multivariate multiple regression, MLR with multiple dependent variables, multiple-outcome regression, Çok Değişkenli Regresyon (MLR — Çoklu DV)
Связанные	5	5
Сводка	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.	Multivariate regression is a linear regression method that predicts several continuous dependent variables at the same time from a shared set of predictors. As developed in standard treatments such as Johnson and Wichern's Applied Multivariate Statistical Analysis (2007), each response equation can be fitted by ordinary least squares while the covariance structure of the residuals is used for joint testing across outcomes.