Estimators can be found by maximizing a likelihood function even if the true probability distribution function doesn't match the chosen family. When estimating first order moments, a condition for consistency can be proven. The estimators become asymptotically normal, with a lower bound for the covariance matrix. This bound can be reached with consistent estimates for second order moments. Consistency in estimating both first and second moments is possible with a certain condition.