Among a number of estimators of the same class, the estimator having the least variance is called an efficient estimator. An estimator whose efficiency tends to unity for an indefinitely increasing number of trials is called . [2] Essentially, a more efficient estimator, experiment, or test needs fewer observations than a less efficient one to achieve a given performance. Efficient: Minimum variance [] This property is what makes the OLS method of estimating α {\displaystyle \alpha } and β {\displaystyle \beta } the best of all other methods. In statistics, an efficient estimator is an estimator that estimates the quantity of interest in some “best possible” manner. When there are more than one unbiased method of estimation to choose from, that estimator which has the lowest variance is best. We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large … In the comparison of various statistical procedures, efficiency is a measure of quality of an estimator, of an experimental design,[1] or of a hypothesis testing procedure. Thus, if we have two estimators $$\widehat {{\alpha _1}}$$ and $$\widehat {{\a

It is clear from (7.9) that if an efficient estimator exists it is unique, as formula (7.9) cannot be valid for two different functions φ. The notion of “best possible” relies upon the choice of a particular loss function — the function which quantifies the relative degree of undesirability of estimation errors of different magnitudes.