A common problem in multienvironment trials are the missing genotype-environmental combinations. Recently, Bergamo proposed a distribution-free multiple imputation method in the interaction matrix. The purpose of this paper is to evaluate the new development and compare it with methodologies that have success in the genotype-environmental trials with missing data, like the alternating least squares (ALS) and the robust estimates, using the Additive Main effects and Multiplicative Interaction Models (AMMI). Was made an simulation study based in real data, doing missed random considering different percentages, imputing the observations and comparing the methodologies through three criteria: the square root of the mean predictive difference, the Procrustes statistic and the Spearman's rank correlation coeficient. Was concluded that the multiple imputation is not better than the imputation based in a additive model without interaction, and the best results for the variance are obtained with robust sub-models. All the considerated methods in this study have a high correlation between the true and the imputed missing values.
In this paper we consider Markov chains associated with the Metropolis-Hastings algorithm.
We propose conditions under which the sequence of the successive densities of such a chain converges to the
target density according to the total variation distance for any choice of the initial density.
In particular we prove that the positiveness of the target and the proposal densities is enough for the chain to