方法对比
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| 最大似然估计× | 结构方程模型× | |
|---|---|---|
| 领域≠ | 统计学 | 研究统计学 |
| 方法族≠ | Regression model | Process / pipeline |
| 起源年份≠ | 1922 | 1921 |
| 提出者≠ | R. A. Fisher | Sewall Wright |
| 类型≠ | Parametric point estimator | Method |
| 开创性文献≠ | Fisher, R. A. (1922). On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society of London, Series A, 222, 309–368. DOI ↗ | Jöreskog, K. G., & Sörbom, D. (1973). LISREL: A general computer program for estimating a linear structural equation system. Research Bulletin 73-5. University of Stockholm. link ↗ |
| 别名 | MLE, maximum-likelihood estimator, ML estimation, Fisher's method of maximum likelihood | SEM, path analysis, latent variable modeling, causal modeling |
| 相关≠ | 4 | 3 |
| 摘要≠ | Maximum Likelihood Estimation (MLE) is a general-purpose parametric method for estimating the unknown parameters of a statistical model by finding the parameter values that make the observed data most probable. Formalized by R. A. Fisher in his landmark 1922 paper in the Philosophical Transactions of the Royal Society, MLE has become the dominant parameter-estimation paradigm in modern statistics and is the foundational engine behind logistic regression, generalized linear models, structural equation modeling, and virtually all parametric inference procedures. | Structural equation modeling (SEM) is a comprehensive statistical framework combining path analysis (Sewall Wright, 1921) and confirmatory factor analysis to test complex causal models linking observed and latent variables. Formalized by Jöreskog (1973) with LISREL software, SEM enables simultaneous estimation of measurement relationships (how variables measure latent constructs) and structural relationships (how constructs influence outcomes), making it powerful for theory testing in psychology, epidemiology, organizational research, and health sciences where complex mediation, moderation, and latent processes require integrated analysis. |
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