Salīdzināt metodes

	Analīze par sabrukuma punktu	Heteroscedasticitātei izturīgi (HC) standartas kļūdas
Nozare	Statistika	Statistika
Saime	Regression model	Regression model
Izcelsmes gads	1983	1980
Autors	Hampel (1971); Donoho & Huber (1983)	Eicker; Huber; White (1980); MacKinnon & White (1985)
Tips	Robustness diagnostic for estimators	Robust covariance estimator for linear regression
Pirmavots	Donoho, D. L. & Huber, P. J. (1983). The Notion of Breakdown Point. In A Festschrift for Erich L. Lehmann (pp. 157-184). Wadsworth.	White, H. (1980). A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica, 48(4), 817-838.
Citi nosaukumi	breakdown point, finite-sample breakdown point, robustness breakdown analysis, Bozunma Noktası Analizi	robust standard errors, White standard errors, Huber-Eicker-White standard errors, sandwich standard errors
Saistītās	5	5
Kopsavilkums	Breakdown point analysis quantifies the fraction of outliers an estimator can tolerate before it produces meaningless results. Formalised by Hampel (1971) and Donoho and Huber (1983), it is the standard tool for comparing the robustness of competing estimators.	Heteroscedasticity-robust standard errors are a correction to the covariance matrix of an OLS regression that yields valid inference when the error variance is not constant. Introduced by Halbert White in 1980 and refined into the finite-sample variants HC1-HC4 by MacKinnon and White in 1985, they leave the coefficient estimates unchanged but rebuild the standard errors so that t and F tests remain trustworthy under heteroscedasticity.