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Lineaarinen erotteluanalyysi (LDA×Monimuuttuja-analyysi varianssille (MANOVA)×
TieteenalaTilastotiedeTilastotiede
MenetelmäperheHypothesis testHypothesis test
Syntyvuosi19361932
KehittäjäRonald A. FisherSamuel Stanley Wilks (Wilks' Lambda, 1932); Roy, Hotelling, Pillai (mid-20th c.)
TyyppiParametric linear classifier / dimensionality reductionParametric multivariate mean comparison
AlkuperäislähdeFisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188. DOI ↗Tabachnick, B.G. & Fidell, L.S. (2013). Using Multivariate Statistics (6th ed.). Pearson. ISBN: 978-0205849574
RinnakkaisnimetLDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysisMultivariate ANOVA, Çok Değişkenli ANOVA (MANOVA)
Liittyvät75
TiivistelmäLinear Discriminant Analysis (LDA) is a parametric supervised classification method that finds the linear combination of continuous predictors that best separates two or more predefined groups. Introduced by Ronald A. Fisher in his landmark 1936 paper on taxonomic measurements, it simultaneously serves as a classifier and a dimensionality-reduction tool, and can be understood as the classification-oriented counterpart of MANOVA.MANOVA is a parametric hypothesis test that simultaneously compares group means across multiple continuous dependent variables, controlling the inflation of Type I error that would result from running separate ANOVAs. Key multivariate test statistics — Wilks' Lambda, Pillai's Trace, Hotelling-Lawley Trace, and Roy's Greatest Root — were developed between the 1930s and 1950s, with Wilks' Lambda formalised by Samuel Stanley Wilks in 1932.
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ScholarGateVertaile menetelmiä: Linear Discriminant Analysis (Classification) · MANOVA. Haettu 2026-06-17 osoitteesta https://scholargate.app/fi/compare