Multivariate One-Way Classification Model With Random Effects

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Multivariate One-Way Classification Model With Random Effects
Schott, James R.
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xz ™ x.
then we need to find the limiting distribution of ch (HE - ) 3 s n as n •> oo. Since we can write E = Y Y', where Y = (y, v n n' n x l (n) — x x win: x trj i —iv .
n (n) (n) % (n) -1 I 2 ' -•• 'le ) and ^i ~ N m ( -^' n ): i = l»2,...,e, inde- pendently, we can restate the problem as that of determining the limiting distribution of ch (H (Y Y' ) ). Consider the s n n following elementary result.
Lemma 3.6.1 : Ifu ~ N(0,l/n), then u — — > u, where u is a degenerate random variable with a
...ll of its probability at zero.
We also need the following results, the first of which is well known as the continuity theorem (see, for example, Breiman [1968:236] ) .
68 Lemma 3.6.2 : Let x, ,x» ,x_ , . . . be a sequence of random vectors. Then x > x if and only if — n — J lim E[exp(ix't)] = E[exp(ix't)] n->x.: i -, j r (n) (n) (n) , , , j = 1 , 2 , . . . ,m, and suppose {x, ,X-> > • • • t i£ •> are mutually independent for all n.


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