Salīdzināt metodes

	Parastā mazāko kvadrātu (OLS) regresija	Robustu kovariācijas novērtēšana (MCD)
Nozare	Ekonometrija	Statistika
Saime	Regression model	Regression model
Izcelsmes gads	2019	1999
Autors	Wooldridge (textbook treatment); classical least squares	Rousseeuw; Rousseeuw & Van Driessen (Fast-MCD)
Tips	Linear regression	Robust multivariate location-scatter estimator
Pirmavots	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Rousseeuw, P. J. & Van Driessen, K. (1999). A Fast Algorithm for the Minimum Covariance Determinant Estimator. Technometrics, 41(3), 212-223.
Citi nosaukumi	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	minimum covariance determinant, MCD estimator, robust covariance estimation, Robust Kovaryans Tahmini (MCD)
Saistītās	5	4
Kopsavilkums	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).	Robust Covariance via the Minimum Covariance Determinant (MCD) estimates a multivariate mean vector and covariance matrix that are not distorted by outliers. It was made practical by the Fast-MCD algorithm of Rousseeuw and Van Driessen (1999), building on Rousseeuw's earlier work on robust estimation.