Optimal discriminant analysis and classification tree analysis information
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Optimal Discriminant Analysis (ODA)[1] and the related classification tree analysis (CTA) are exact statistical methods that maximize predictive accuracy. For any specific sample and exploratory or confirmatory hypothesis, optimal discriminant analysis (ODA) identifies the statistical model that yields maximum predictive accuracy, assesses the exact Type I error rate, and evaluates potential cross-generalizability. Optimal discriminant analysis may be applied to > 0 dimensions, with the one-dimensional case being referred to as UniODA and the multidimensional case being referred to as MultiODA. Optimal discriminant analysis is an alternative to ANOVA (analysis of variance) and regression analysis.
^Provider: John Wiley & Sons, Ltd
Content:text/plain; charset="UTF-8"
TY - JOUR
AU - Yarnold, Paul R.
AU - Soltysik, Robert C.
TI - Theoretical Distributions of Optima for Univariate Discrimination of Random Data*
JO - Decision Sciences
VL - 22
IS - 4
PB - Blackwell Publishing Ltd
SN - 1540-5915
UR - https://dx.doi.org/10.1111/j.1540-5915.1991.tb00362.x
DO - 10.1111/j.1540-5915.1991.tb00362.x
SP - 739
EP - 752
KW - Discrete Programming
KW - Linear Statistical Models
KW - Mathematical Programming
KW - and Statistical Techniques
PY - 1991
ER -1.tb00362.x
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