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The problem of adequate sample size in pattern recognition studies: The multivariate normal case
Author(s) -
Han J. H.,
Ward A. J. I.,
Lavine B. K.
Publication year - 1990
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1180040110
Subject(s) - multivariate statistics , multivariate normal distribution , parametric statistics , sample size determination , computer science , multivariate analysis , monte carlo method , statistics , sample (material) , population , pattern recognition (psychology) , artificial intelligence , data mining , mathematics , medicine , chromatography , chemistry , environmental health
Because many pattern recognition techniques are predicated on the assumption of mutivariate normal data, Monte Carlo simulation studies were performed to determine the number of samples that are necessary to describe a multivariate normal population adequately. From these studies we have learned that hundreds of samples are required. These results suggest that parametric procedures should only be used to analyze very large data sets.