July 21, 2015 1 Comment
Today’s Statistics Lesson of the Day will discuss Basu’s theorem, which connects the previously discussed concepts of minimally sufficient statistics, complete statistics and ancillary statistics. As before, I will begin with the following set-up.
Suppose that you collected data
in order to estimate a parameter . Let be the probability density function (PDF) or probability mass function (PMF) for .
be a statistics based on .
Basu’s theorem states that, if is a complete and minimal sufficient statistic, then is independent of every ancillary statistic.
Establishing the independence between 2 random variables can be very difficult if their joint distribution is hard to obtain. This theorem allows the independence between minimally sufficient statistic and every ancillary statistic to be established without their joint distribution – and this is the great utility of Basu’s theorem.
However, establishing that a statistic is complete can be a difficult task. In a later lesson, I will discuss another theorem that will make this task easier for certain cases.