Some of the properties are defined relative to a class of candidate estimators, a set of possible T(") that we will denote by T. The density of an estimator T(") will be denoted (t, o), or when it is necessary to index the estimator, T(t, o). 1.1 Unbiasness. An estimator is a function of the data. Example: Suppose X 1;X 2; ;X n is an i.i.d. If ^(x) is a maximum likelihood estimate for , then g( ^(x)) is a maximum likelihood estimate for g( ). Then relative e ciency of ^ 1 relative to ^ 2, Relative e ciency: If ^ 1 and ^ 2 are both unbiased estimators of a parameter we say that ^ 1 is relatively more e cient if var(^ 1)