Порівняння методів

	Факторний аналіз	Метод головних компонент	Оцінювання робастної коваріації (MCD)
Галузь	Статистика досліджень	Машинне навчання	Статистика
Родина	Process / pipeline	Machine learning	Regression model
Рік появи	1931	2002	1999
Автор методу	Louis Leon Thurstone	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Rousseeuw; Rousseeuw & Van Driessen (Fast-MCD)
Тип	Method	Unsupervised dimensionality reduction	Robust multivariate location-scatter estimator
Основоположне джерело	Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press.	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.
Інші назви	EFA, CFA, latent variable modeling	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	minimum covariance determinant, MCD estimator, robust covariance estimation, Robust Kovaryans Tahmini (MCD)
Пов'язані	3	3	4
Підсумок	Factor analysis is a statistical technique for identifying latent (unobserved) dimensions underlying observed variables, developed by Louis Leon Thurstone in the 1930s and formalized by Jöreskog (1969). Exploratory factor analysis (EFA) discovers unknown factor structure from data; confirmatory factor analysis (CFA) tests hypothesized relationships between observed and latent variables. Essential in psychometrics (test development), organizational research (measuring constructs like leadership style), and biomedicine (identifying disease subtypes), factor analysis reduces dimensionality while revealing conceptual organization in multivariate data.	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.