summary.In Bonneu (1988) a prediction criterion for model selection is defined for Generalized Linear Models (GLM). This criterion, similar to AIC (Akaike, H. (1973)), Sakamoto, Y. and Akaike, H. (1978)) is based on the expected Kullback-Leibler discrepancy (Kullback, S. 1959)), especially defined for prediction. The model selection strategy is employed to select a Nelder's link function (1972) and a small subset of explanatory variables. Data are observed from an experimental design with unequal numbers of replications. This paper deals with the asymptotic estimate of this prediction criterion and compares it with a simulated bootstrap estimate. Usually asymptotic criteria for model selection can be written as the sum of a statistic and a bias (see Linhart, H. and Volkers, P. (1984); Linhart, H. and Zucchini, W. (1986)). In the present paper, asymptotic arguments are investigated in a different way, taking into account the prediction objective and GLM framework with unequal numbers of replicates.