Efficient estimators are always minimum variance unbiased estimators. Information such as&colon; estimation, sufficiency and properties of variances of estimators. The construction and comparison of estimators are the subjects of the estimation theory. All statistical hypothesis tests and all statistical estimators are derived via statistical models. This approach allows for more natural study of the asymptotic properties of the estimators. Use of linear-drift estimators such as the Hadamard variance could also be employed. Population parameters are estimated with many point estimators. They have however similar efficiency as the maximum likelihood estimators. An alternative to relative efficiency for comparing estimators, is the Pitman closeness criterion. Trimmed estimators and Winsorised estimators are general methods to make statistics more robust. His derivation included the joint limiting distribution of the least squares estimators of two parameters. There are a number of possible Monte Carlo estimators available for both indices. M-estimators are a generalization of maximum likelihood estimators (MLEs). The general class of estimators that arise as minimizers of sums are called M-estimators. Maximum likelihood estimators (MLE) are thus a special case of M-estimators.