In statistics, Cochran's theorem, devised by William G. Cochran, is a theorem used to justify results relating to the probability distributions of statistics that are used in the analysis of variance.
Suppose U1, ..., UN are i.i.d. standard normally distributed random variables, and are positive semidefinite matrices with . Further suppose that , where ri is the rank of . If we write
so that the Qi are quadratic forms, then Cochran's theorem states that the Qi are independent, and each Qi has a chi-squared distribution with ri degrees of freedom.
Less formally, it is the number of linear combinations included in the sum of squares defining Qi, provided that these linear combinations are linearly independent.
We first show that the matrices B(i) can be simultaneously diagonalized and that their non-zero eigenvalues are all equal to +1. We then use the vector basis that diagonalize them to simplify their characteristic function and show their independence and distribution.
Each of the matrices B(i) has rank ri and thus ri non-zero eigenvalues. For each i, the sum has at most rank . Since , it follows that C(i) has exactly rank N − ri.
Therefore B(i) and C(i) can be simultaneously diagonalized. This can be shown by first diagonalizing B(i). In this basis, it is of the form:
Thus the lower rows are zero. Since , it follows that these rows in C(i) in this basis contain a right block which is a unit matrix, with zeros in the rest of these rows. But since C(i) has rank N − ri, it must be zero elsewhere. Thus it is diagonal in this basis as well. It follows that all the non-zero eigenvalues of both B(i) and C(i) are +1. Moreover, the above analysis can be repeated in the diagonal basis for . In this basis is the identity of an vector space, so it follows that both B(2) and are simultaneously diagonalizable in this vector space (and hence also together with B(1)). By iteration it follows that all B-s are simultaneously diagonalizable.
Thus there exists an orthogonal matrix such that for all , is diagonal, where any entry with indices , , is equal to 1, while any entry with other indices is equal to 0.
Let denote some specific linear combination of all after transformation by . Note that due to the length preservation of the orthogonal matrix S, that the Jacobian of a linear transformation is the matrix associated with the linear transformation itself, and that the determinant of an orthogonal matrix has modulus 1.
The characteristic function of Qi is:
This is the Fourier transform of the chi-squared distribution with ri degrees of freedom. Therefore this is the distribution of Qi.
Moreover, the characteristic function of the joint distribution of all the Qis is:
From this it follows that all the Qis are independent.
Sample mean and sample variance
If X1, ..., Xn are independent normally distributed random variables with mean μ and standard deviation σ then
is standard normal for each i. Note that the total Q is equal to sum of squared Us as shown here:
which stems from the original assumption that .
So instead we will calculate this quantity and later separate it into Qi's. It is possible to write
(here is the sample mean). To see this identity, multiply throughout by and note that
and expand to give
The third term is zero because it is equal to a constant times
and the second term has just n identical terms added together. Thus
Now with the matrix of ones which has rank 1. In turn given that . This expression can be also obtained by expanding in matrix notation. It can be shown that the rank of is as the addition of all its rows is equal to zero. Thus the conditions for Cochran's theorem are met.
Cochran's theorem then states that Q1 and Q2 are independent, with chi-squared distributions with n − 1 and 1 degree of freedom respectively. This shows that the sample mean and sample variance are independent. This can also be shown by Basu's theorem, and in fact this property characterizes the normal distribution – for no other distribution are the sample mean and sample variance independent.
The result for the distributions is written symbolically as
Both these random variables are proportional to the true but unknown variance σ2. Thus their ratio does not depend on σ2 and, because they are statistically independent. The distribution of their ratio is given by
where F1,n − 1 is the F-distribution with 1 and n − 1 degrees of freedom (see also Student's t-distribution). The final step here is effectively the definition of a random variable having the F-distribution.
Estimation of variance
To estimate the variance σ2, one estimator that is sometimes used is the maximum likelihood estimator of the variance of a normal distribution
Cochran's theorem shows that
and the properties of the chi-squared distribution show that
The following version is often seen when considering linear regression. Suppose that is a standard multivariate normal random vector (here denotes the n-by-n identity matrix), and if are all n-by-n symmetric matrices with . Then, on defining , any one of the following conditions implies the other two:
- (thus the are positive semidefinite)
- is independent of for
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. (July 2011)