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Лінійний дискримінантний аналіз (LDA×Багатовимірний дисперсійний аналіз (MANOVA)×
ГалузьСтатистикаСтатистика
РодинаHypothesis testHypothesis test
Рік появи19361932
Автор методуRonald A. FisherSamuel Stanley Wilks (Wilks' Lambda, 1932); Roy, Hotelling, Pillai (mid-20th c.)
ТипParametric linear classifier / dimensionality reductionParametric multivariate mean comparison
Основоположне джерелоFisher, 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
Інші назвиLDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysisMultivariate ANOVA, Çok Değişkenli ANOVA (MANOVA)
Пов'язані75
ПідсумокLinear 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.
ScholarGateНабір даних
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  2. 1 Джерела
  3. PUBLISHED
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ScholarGateПорівняння методів: Linear Discriminant Analysis (Classification) · MANOVA. Отримано 2026-06-17 з https://scholargate.app/uk/compare