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Optimal discriminant analysis and classification tree analysis

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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.

See also

References

  1. 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

Notes

External links

de:Diskriminanzanalyse eo:Vikipedio:Projekto matematiko/Lineara diskriminanta analitiko fr:Analyse discriminante linéaire hr:Linearna analiza različitih it:Analisi discriminante nl:Discriminantanalyse ja:判別分析 pl:Liniowa analiza dyskryminacyjna ru:Дискриминантный анализ sl:Diskriminantna analiza zh:線性判別分析




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