পদ্ধতির তুলনা করুন

	প্রভাব নির্ণায়ক (কুকের দূরত্ব, DFFITS, লিভারেজ)	প্রধান উপাদান বিশ্লেষণ	Robust Covariance Estimation (MCD)
ক্ষেত্র	পরিসংখ্যান	যন্ত্র শিখন	পরিসংখ্যান
পরিবার	Regression model	Machine learning	Regression model
উদ্ভবের বছর	1977	2002	1999
প্রবর্তক	R. Dennis Cook (Cook's distance); Belsley, Kuh & Welsch (DFFITS, leverage)	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Rousseeuw; Rousseeuw & Van Driessen (Fast-MCD)
ধরন	Regression diagnostic	Unsupervised dimensionality reduction	Robust multivariate location-scatter estimator
মৌলিক উৎস	Cook, R. D. (1977). Detection of Influential Observations in Linear Regression. Technometrics, 19(1), 15-18.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.	Rousseeuw, P. J. & Van Driessen, K. (1999). A Fast Algorithm for the Minimum Covariance Determinant Estimator. Technometrics, 41(3), 212-223.
অপর নাম	Cook's distance, DFFITS, leverage, influential observation detection	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	minimum covariance determinant, MCD estimator, robust covariance estimation, Robust Kovaryans Tahmini (MCD)
সম্পর্কিত	5	3	4
সারসংক্ষেপ	Influence diagnostics are a family of post-fit measures that quantify how much each single observation affects a fitted regression. Cook's distance was introduced by R. Dennis Cook in 1977, with leverage and DFFITS formalised by Belsley, Kuh and Welsch in 1980, to flag the observations that most strongly pull the estimated coefficients.	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.	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.