Comparing Double Minimization and Zigzag Algorithms in Joint Regression Analysis: the Complete Case

Abstract

Joint Regression Analysis is a widely used technique for cultivar comparison. For each cultivar a linear regression is adjusted on a non observable regressor: the environmental index. This index measures, for each block, the corresponding productivity. When all cultivars are present in all the blocks in the field trials the series of experiments is complete. To carry out the minimization of the sum of sums of squares of residuals in order to estimate the coefficients of the regressions and the environmental indexes an iterative algorithm, the zigzag algorithm, see Mexia et al. (1999), was introduced. This algorithm performs well, see, e.g., Mexia et al. (2001) and Mexia & Pereira (2001), but it has not been shown that it converges to the absolute minimum of the goal function. We presented, see Pereira & Mexia (2007) an alternative algorithm and showed that, in the complete case, it converges to the absolute minimum. Through an example it was shown that the results obtained using both algorithms agreed. We now analyse the reason behind the agreement between both algorithms.