विधियों की तुलना करें

	क्लस्टर-मजबूत मानक त्रुटियाँ	साधारण न्यूनतम वर्ग (OLS) समाश्रयण
क्षेत्र	सांख्यिकी	अर्थमिति
परिवार	Regression model	Regression model
उद्भव वर्ष	1986	2019
प्रवर्तक	Liang & Zeger (GEE sandwich); Cameron & Miller (practitioner synthesis)	Wooldridge (textbook treatment); classical least squares
प्रकार	Robust variance estimation for regression	Linear regression
मौलिक स्रोत	Liang, K. Y. & Zeger, S. L. (1986). Longitudinal Data Analysis Using Generalized Linear Models. Biometrika, 73(1), 13-22.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
उपनाम	clustered standard errors, cluster-robust inference, clustered variance estimator, Küme Robust Standart Hatalar	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
संबंधित	4	5
सारांश	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).