QUADRATIC DISCRIMINATION THROUGH ORTHOGONAL TRANSFORMATION AND ITS APPLICATION TO LONG-RANGE FORECASTING OF DROUGHT AND EXCESSIVE RAINFALL

Shi Neng; Chen Jiyi

doi:10.1007/BF02680051

Impact Factor: 5.8

Volume 3 Issue 1

Jan. 1986

Article Contents

Article > ADVANCES IN ATMOSPHERIC SCIENCES > 1986 > 3(1): 125-133

QUADRATIC DISCRIMINATION THROUGH ORTHOGONAL TRANSFORMATION AND ITS APPLICATION TO LONG-RANGE FORECASTING OF DROUGHT AND EXCESSIVE RAINFALL

Shi Neng,
Chen Jiyi

1.
Nanjing Institute of Meteorology, Nanjing,Nanjing Agricultural University, Nanjing

Shi Neng,
Chen Jiyi

doi: 10.1007/BF02680051

Abstract
References
Related papers
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Abstract:
The method of quadratic discrimination through orthogonal transformation is introduced to optimize quadratic discriminant function, which has been proved more effective than the method of stepwise multiple discrimination. It is noteworthy that, in the case of a large number of predictors, it is better to make a preliminary choice by using stepwise multiple discrimination or stepwise regression so that the calculation can be made more stable and the effectiveness of discrimination can be improved.
References

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Manuscript History

Manuscript received: 10 January 1986

Manuscript revised: 10 January 1986

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

QUADRATIC DISCRIMINATION THROUGH ORTHOGONAL TRANSFORMATION AND ITS APPLICATION TO LONG-RANGE FORECASTING OF DROUGHT AND EXCESSIVE RAINFALL

Shi Neng,
Chen Jiyi

1. Nanjing Institute of Meteorology, Nanjing,Nanjing Agricultural University, Nanjing

Manuscript received: 1986-01-10
Manuscript revised: 1986-01-10

Abstract: The method of quadratic discrimination through orthogonal transformation is introduced to optimize quadratic discriminant function, which has been proved more effective than the method of stepwise multiple discrimination. It is noteworthy that, in the case of a large number of predictors, it is better to make a preliminary choice by using stepwise multiple discrimination or stepwise regression so that the calculation can be made more stable and the effectiveness of discrimination can be improved.