THE NONLINEAR DISCRIMINANT AND STEPWISE NONLINEAR DISCRIMINANT ANALYSES

Yao Dirong

doi:10.1007/BF02657343

Impact Factor: 5.8

Volume 5 Issue 1

Jan. 1988

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Article > ADVANCES IN ATMOSPHERIC SCIENCES > 1988 > 5(1): 27-34

THE NONLINEAR DISCRIMINANT AND STEPWISE NONLINEAR DISCRIMINANT ANALYSES

Yao Dirong

1.
Department of Geography, Hangzhou University, Hangzhou

Yao Dirong

doi: 10.1007/BF02657343

Abstract
References
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Abstract:
The nonlinear discriminant function, when covariance matrixes of each population are not equal to each other, is discussed on the basis of Bayes criterion, and by using the stepwise discriminant method, a method for calculating the nonlinear discriminant function is provided, which is called stepwise nonlinear discriminant analysis. In addition, an appropriate discriminant analysis model is selected by testing whether the covariance matrixes of each population are equal, which was proposed by Box. The calculations show that, the discrimi-nant effects of this method are superior not only to linear discriminant analysis, but also to nonlinear discrimi-nant analysis in which the stepwise discriminant algorithm is not used when covariance matrixes of each popu-lation are not equal to each other. Satisfactory results have been obtained in applying this method. This is an important improvement on the linear discriminant analysis used in the weather typing prediction at present.
References

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Manuscript History

Manuscript received: 10 January 1988

Manuscript revised: 10 January 1988

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

THE NONLINEAR DISCRIMINANT AND STEPWISE NONLINEAR DISCRIMINANT ANALYSES

Yao Dirong

1. Department of Geography, Hangzhou University, Hangzhou

Manuscript received: 1988-01-10
Manuscript revised: 1988-01-10

Abstract: The nonlinear discriminant function, when covariance matrixes of each population are not equal to each other, is discussed on the basis of Bayes criterion, and by using the stepwise discriminant method, a method for calculating the nonlinear discriminant function is provided, which is called stepwise nonlinear discriminant analysis. In addition, an appropriate discriminant analysis model is selected by testing whether the covariance matrixes of each population are equal, which was proposed by Box. The calculations show that, the discrimi-nant effects of this method are superior not only to linear discriminant analysis, but also to nonlinear discrimi-nant analysis in which the stepwise discriminant algorithm is not used when covariance matrixes of each popu-lation are not equal to each other. Satisfactory results have been obtained in applying this method. This is an important improvement on the linear discriminant analysis used in the weather typing prediction at present.