Comparar métodos

	Erros Padrão Robustos a Clusters	Teste de Permutação (Randomização)
Área	Estatística	Estatística
Família	Regression model	Regression model
Ano de origem	1986	2005
Autor original	Liang & Zeger (GEE sandwich); Cameron & Miller (practitioner synthesis)	Good (2005); Edgington & Onghena (2007); resampling tradition
Tipo	Robust variance estimation for regression	Nonparametric resampling test
Fonte seminal	Liang, K. Y. & Zeger, S. L. (1986). Longitudinal Data Analysis Using Generalized Linear Models. Biometrika, 73(1), 13-22.	Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792
Outros nomes	clustered standard errors, cluster-robust inference, clustered variance estimator, Küme Robust Standart Hatalar	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
Relacionados	4	5
Resumo	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.	The permutation test is a nonparametric resampling procedure that builds the sampling distribution of a test statistic directly from the data by repeatedly shuffling the group labels. Developed in the resampling tradition and treated systematically by Good (2005) and Edgington & Onghena (2007), it requires no parametric distributional assumption and yields an exact p-value.