Model Evaluation by Comparison of Model‐Based Predictions and Measured Values

Agronomy Journal - Tập 95 Số 6 - Trang 1442-1446 - 2003
Hugh G. Gauch1, J. T. Gene Hwang2, Gary W. Fick1
1Crop and Soil Sciences, Cornell Univ., Ithaca, NY 14853
2Dep. of Mathematics and Dep. of Statistical Science Cornell Univ. Ithaca NY 14853

Tóm tắt

The appropriateness of a statistical analysis for evaluating a model depends on the model's purpose. A common purpose for models in agricultural research and environmental management is accurate prediction. In this context, correlation and linear regression are frequently used to test or compare models, including tests of intercept a = 0 and slope b = 1, but unfortunately such results are related only obliquely to the specific matter of predictive success. The mean squared deviation (MSD) between model predictions X and measured values Y has been proposed as a directly relevant measure of predictive success, with MSD partitioned into three components to gain further insight into model performance. This paper proposes a different and better partitioning of MSD: squared bias (SB), nonunity slope (NU), and lack of correlation (LC). These MSD components are distinct and additive, they have straightforward geometric and analysis of variance (ANOVA) interpretations, and they relate transparently to regression parameters. Our MSD components are illustrated using several models for wheat (Triticum aestivum L.) yield. The MSD statistic and its components nicely complement correlation and linear regression in evaluating the predictive accuracy of models.

Từ khóa


Tài liệu tham khảo

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Thông tin
Thông tin xuất bản

Agronomy Journal

Tập 95 Số 6

1442-1446

Thông tin tác giả