方法对比
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| 贝叶斯结构方程模型 (BSEM)× | 普通最小二乘法 (OLS) 回归× | |
|---|---|---|
| 领域≠ | 贝叶斯 | 计量经济学 |
| 方法族≠ | Bayesian methods | Regression model |
| 起源年份≠ | 2012 | 2019 |
| 提出者≠ | Bengt Muthén & Tihomir Asparouhov | Wooldridge (textbook treatment); classical least squares |
| 类型≠ | Bayesian latent variable model | Linear regression |
| 开创性文献≠ | Muthén, B. & Asparouhov, T. (2012). Bayesian SEM: A More Flexible Representation of Substantive Theory. Psychological Methods, 17(3), 313–335. link ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 别名 | BSEM, Bayesian latent variable model, approximate zero constraints SEM, Bayesçi Yapısal Eşitlik Modeli | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 相关≠ | 6 | 5 |
| 摘要≠ | Bayesian SEM, introduced by Muthén and Asparouhov in 2012, extends classical structural equation modeling by placing prior distributions on factor loadings, path coefficients, and covariances. Instead of returning a single maximum-likelihood estimate, it uses Markov chain Monte Carlo to produce a full posterior distribution for every parameter, enabling principled uncertainty quantification in models with latent variables. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
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