Let $\hat { \theta } _ { 1 } , \hat { \theta } _ { 2 } , \cdots \cdots , \hat { \theta } _ { n }$

Short Answer

Let $\hat { \theta } _ { 1 } , \hat { \theta } _ { 2 } , \cdots \cdots , \hat { \theta } _ { n }$ be the maximum likelihood estimates (mle's) of the parameters $\theta _ { 1 } , \theta _ { 2 } , \cdots \cdots , \theta _ { n }$ . Then the mle of any function h( $\theta _ { 1 } , \theta _ { 2 } , \cdots \cdots , \theta _ { n }$ ) of these parameters is the function $h \left( \hat { \theta } _ { 1 } , \hat { \theta } _ { 2 } , \cdots \cdots , \hat { \theta } _ { m } \right)$ of the mle's. This result is known as the __________ principle.