পদ্ধতির তুলনা করুন

	Robust Covariance Estimation (MCD)	Least Trimmed Squares (LTS) Regression
ক্ষেত্র	পরিসংখ্যান	পরিসংখ্যান
পরিবার	Regression model	Regression model
উদ্ভবের বছর	1999	1984
প্রবর্তক	Rousseeuw; Rousseeuw & Van Driessen (Fast-MCD)	Peter J. Rousseeuw
ধরন	Robust multivariate location-scatter estimator	Robust linear regression
মৌলিক উৎস	Rousseeuw, P. J. & Van Driessen, K. (1999). A Fast Algorithm for the Minimum Covariance Determinant Estimator. Technometrics, 41(3), 212-223.	Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880.
অপর নাম	minimum covariance determinant, MCD estimator, robust covariance estimation, Robust Kovaryans Tahmini (MCD)	LTS, least trimmed squares regression, trimmed least squares, robust regression
সম্পর্কিত	4	5
সারসংক্ষেপ	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.	Least Trimmed Squares is a robust linear regression method introduced by Peter J. Rousseeuw in 1984. Instead of fitting all residuals, it estimates the coefficients by minimising the sum of only the h smallest squared residuals, which gives it a breakdown point of up to 50% and reliable estimates on data heavily contaminated by outliers.