Multivariate One-Way Classification Model With Random Effects
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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