Harris (Biometrika, 1989) suggests a predictive distribution based on bootstrapping using the maximum likelihood estimator of an unknown parameter. Basu and Harris (Biometrika, 1994) introduce robust estimative and bootstrap predictive distributions for discrete models by using the minimum Hellinger distance estimator of the unknown parameter instead of the maximum likelihood estimator. Generalizing the results of Basu and Harris, the present paper considers parametric predictive distributions using the minimumpenalized blended weight Hellinger distanceestimator for discrete models. Monte Carlo siniulations suggest that the proposed predictive distributions are attractive robust substitutes for the usual predictive distributions based on the maximum likelihood estimator under data contamination, and perform favorably compared to the predictive distributions suggested by Basu and Harris