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This is a very important theorem which allows us to
decompose sums of squares into several quadratic forms and identify their
distributions and establish their independence. It can be used to great
advantage in Analysis of Variance and Regression.
The importance of the terms in the model
is assessed via the distributions of their sums of squares.
Theorem
4..7
Given
, suppose that
is
decomposed into
quadratic forms,
,
, where the rank of
is
and the
are positive semidefinite, then any one of the following
conditions implies the other two.
- (a)
- the ranks of the
add to
;
- (b)
- each
;
- (c)
- all the
are mutually independent.
Proof We can write
That is,
- (i)
- Given (a) we will prove (b).
Select an arbitrary
, say
. If we
make an orthogonal transformation
which diagonalizes
, we obtain from
Since the first and last terms are diagonal, so is the
second. Since
and therefore
,
of the leading diagonal elements of
are zero. Thus the
corresponding elements of
are
and since by (a) the
rank of
is
, the other elements of its leading
diagonal are
and the corresponding elements of
are
.
Hence from Theorem 4.4,
and
is idempotent.
The same result holds for the other
and we have established
(b) from (a).
- (ii)
- Given (b) we will prove (c).
 |
(4.10) |
and (b) implies that each
is idempotent
(with rank
). Choose an arbitrary
, say
. There
is an orthogonal matrix
such that
Premultiplying (4.10) by
and post-multiplying by
,
we have
Now each
is idempotent and can't have any negative
elements on its diagonal. So
must have the first
leading diagonal elements
, and submatrices for rows
, columns
and for rows
, columns
must have all elements
. So
and thus
which can
only be so if
.
Since
was arbitrarily chosen, we have proved (c) from (b).
- (iii)
- Given (b) we will prove (a).
If (b) holds,
has
eigenvalues
and
zero
and since
, taking traces we have
.
- (iv)
- Given (c) we will prove (b).
If (c) holds, taking powers of
, we
have
for all positive integers
.
Taking traces we have
This can hold if and only if every eigenvalue of
is
.
That is, if each
.
So we have proved (b) from (c).
A more general version of Cochran's Theorem is stated (without proof)
in Theorem 4.8.
Theorem
4..8
Given
, suppose that
is decomposed into
quadratic forms,
,
, when
. Then
are mutually independent and
if and
only if
.
Example
4..2
We will consider again Example 4.4 from the point of view of Cochran's
Theorem. Recall that
are iid
and
That is,
where
is defined in the usual way. Equivalently,
where
and
are defined in Example 4.1.
We can apply Cochran's Theorem, noting that we can easily show that (a) is true,
since
,
and
. So we may conclude
that
and that
and
are independent.
Next: Order Statistics
Up: Multivariate Normal Distribution
Previous: The role of c.g.f.
  Contents
Bob Murison
2000-10-31