Abstract

Several methods of estimating error rates in Discriminant Analysis are evaluated by sampling methods. Multivariate normal samples are generated on a computer which have various true probabilities of misclassification for different combinations of sample sizes and different numbers of parameters. The two methods in most common use are found to be significantly poorer than some new methods that are proposed.

Keywords

Linear discriminant analysisStatisticsMultivariate statisticsDiscriminantOptimal discriminant analysisMathematicsSampling (signal processing)Word error rateSampling errorMultivariate analysisSample size determinationSample (material)Pattern recognition (psychology)Computer scienceArtificial intelligenceObservational error

Affiliated Institutions

Related Publications

Bootstrap Methods: Another Look at the Jackknife

We discuss the following problem: given a random sample $\\mathbf{X} = (X_1, X_2, \\cdots, X_n)$ from an unknown probability distribution $F$, estimate the sampling distribution...

1979 The Annals of Statistics 16966 citations

Publication Info

Year
1968
Type
article
Volume
10
Issue
1
Pages
1-11
Citations
1480
Access
Closed

External Links

Social Impact

Social media, news, blog, policy document mentions

Citation Metrics

1480
OpenAlex

Cite This

Peter A. Lachenbruch, M. R. Mickey (1968). Estimation of Error Rates in Discriminant Analysis. Technometrics , 10 (1) , 1-11. https://doi.org/10.1080/00401706.1968.10490530

Identifiers

DOI
10.1080/00401706.1968.10490530