In statistics, Basu's theorem states that any boundedly complete minimal sufficient statistic is independent of any ancillary statistic.
This is a 1955 result of Debabrata Basu.
[1] It is often used in statistics as a tool to prove independence of two statistics, by first demonstrating one is complete sufficient and the other is ancillary, then appealing to the theorem.
[2] An example of this is to show that the sample mean and sample variance of a normal distribution are independent statistics, which is done in the Example section below.
This property (independence of sample mean and sample variance) characterizes normal distributions.
be a family of distributions on a measurable space
to some measurable space
is a boundedly complete sufficient statistic for
θ
θ
θ
θ
θ
θ
be the marginal distributions of
the preimage of a set
For any measurable set
θ
θ
θ
Therefore Note the integrand (the function inside the integral) is a function of
is boundedly complete the function is zero for
Let X1, X2, ..., Xn be independent, identically distributed normal random variables with mean μ and variance σ2.
Then with respect to the parameter μ, one can show that the sample mean, is a complete and sufficient statistic – it is all the information one can derive to estimate μ, and no more – and the sample variance, is an ancillary statistic – its distribution does not depend on μ.
Therefore, from Basu's theorem it follows that these statistics are independent conditional on
This independence result can also be proven by Cochran's theorem.
Further, this property (that the sample mean and sample variance of the normal distribution are independent) characterizes the normal distribution – no other distribution has this property.