Abstract
Spatial analysis procedures based on one-dimensional and two-dimensional (separable) ARIMA (Auto Regressive Integrated Moving Average) processes were used to analyze several yield trials. Two criteria were used to determine the best spatial model: 1) standard error of the treatment difference (SED) and 2) mean squared error (MSE) of prediction based on a cross-validation approach. It is found that spatial models with two-dimensional exponential covariance functions are frequently the best models regarding SED and MSE. Differenced models are frequently the best models regarding SED and the worst with respect to MSE.
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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Grondona, M. O.; Crossa, J.; Fox, P. N.; and Pfeiffer, W. H.
(1993).
"SPATIAL ANALYSIS OF YIELD TRIALS USING SEPARABLE ARIMA PROCESSES,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1373
SPATIAL ANALYSIS OF YIELD TRIALS USING SEPARABLE ARIMA PROCESSES
Spatial analysis procedures based on one-dimensional and two-dimensional (separable) ARIMA (Auto Regressive Integrated Moving Average) processes were used to analyze several yield trials. Two criteria were used to determine the best spatial model: 1) standard error of the treatment difference (SED) and 2) mean squared error (MSE) of prediction based on a cross-validation approach. It is found that spatial models with two-dimensional exponential covariance functions are frequently the best models regarding SED and MSE. Differenced models are frequently the best models regarding SED and the worst with respect to MSE.
