Regression model
最大似然估计
最大似然估计（MLE）是一种通用的参数化方法，通过寻找使观测数据最有可能出现的参数值来估计统计模型中未知的参数。该方法由R. A. Fisher在其1922年发表于《英国皇家学会哲学汇刊》的里程碑式论文中正式提出，已成为现代统计学中主流的参数估计范式，是逻辑回归、广义线性模型、结构方程模型以及几乎所有参数推断过程的基石。
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来源
- 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: 10.1098/rsta.1922.0009 ↗
- Casella, G., & Berger, R. L. (2002). Statistical Inference (2nd ed.). Duxbury Press / Cengage Learning. ISBN: 978-0534243128
如何引用本页
ScholarGate. (2026, June 3). Maximum Likelihood Estimation. ScholarGate. https://scholargate.app/zh/statistics/maximum-likelihood-estimation
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