Maximum likelihood estimation of the binary Coefficient of Determination

The binary Coefficient of Determination (CoD) is a key component of inference methods in Genomic Signal Processing. Assuming a stochastic logic model, we introduce a new sample CoD estimator based upon maximum likelihood (ML) estimation. Experiments have been conducted to assess how the ML CoD estimator performs in recovering predictors in multivariate prediction settings. Performance is compared with the traditional nonparametric CoD estimators based on resubstitution, leave-one-out, bootstrap and cross-validation. The results show that the ML CoD estimator is the estimator of choice if prior knowledge is available about the logic relationships in the model, even if this knowledge is incomplete.