Salīdzināt metodes

	Robustā kopu analīze (TCLUST)	Standartkļūdas, kas ir izturīgas pret klasteru ietekmi
Nozare	Statistika	Statistika
Saime	Regression model	Regression model
Izcelsmes gads	2008	1986
Autors	García-Escudero, Gordaliza, Matrán & Mayo-Iscar (TCLUST)	Liang & Zeger (GEE sandwich); Cameron & Miller (practitioner synthesis)
Tips	Robust model-based clustering	Robust variance estimation for regression
Pirmavots	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.	Liang, K. Y. & Zeger, S. L. (1986). Longitudinal Data Analysis Using Generalized Linear Models. Biometrika, 73(1), 13-22.
Citi nosaukumi	TCLUST, trimmed clustering, robust clustering, Robust Küme Analizi (TCLUST)	clustered standard errors, cluster-robust inference, clustered variance estimator, Küme Robust Standart Hatalar
Saistītās	5	4
Kopsavilkums	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.