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| 鲁棒聚类分析 (TCLUST)× | 聚类稳健标准误× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2008 | 1986 |
| 提出者≠ | García-Escudero, Gordaliza, Matrán & Mayo-Iscar (TCLUST) | Liang & Zeger (GEE sandwich); Cameron & Miller (practitioner synthesis) |
| 类型≠ | Robust model-based clustering | Robust variance estimation for regression |
| 开创性文献≠ | García-Escudero, L. A., Gordaliza, A., Matrán, C., & Mayo-Iscar, A. (2008). A General Trimming Approach to Robust Cluster Analysis. The Annals of Statistics, 36(3), 1324-1345. DOI ↗ | Liang, K. Y. & Zeger, S. L. (1986). Longitudinal Data Analysis Using Generalized Linear Models. Biometrika, 73(1), 13-22. DOI ↗ |
| 别名 | TCLUST, trimmed clustering, robust clustering, Robust Küme Analizi (TCLUST) | clustered standard errors, cluster-robust inference, clustered variance estimator, Küme Robust Standart Hatalar |
| 相关≠ | 5 | 4 |
| 摘要≠ | Robust Cluster Analysis is a trimmed model-based clustering method, introduced by García-Escudero and colleagues in 2008, that partitions continuous multivariate data into clusters while resisting the influence of outliers and noise. By setting aside a fraction of the most discordant observations, it keeps the recovered cluster structure from being contaminated by stray points. | Cluster-robust standard errors correct the variance of regression coefficients when observations are correlated within clusters such as schools, hospitals, or regions. The clustered sandwich estimator grew out of Liang & Zeger's (1986) generalized estimating equations and was synthesized for applied work by Cameron & Miller (2015), delivering valid inference when ordinary standard errors would be too small. |
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