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identically distributed, so J$_{i}$ = J for all i and the time grid ( t 1 , . . . , t$_{ν}$ ) is the same for all individuals. The asymptotic distribution of the estimators will be derived for I → ∞ and J fixed, or in practical terms, for “large I and small J ”; this is the usual situation in random-effect one-way ANOVA models. 

Under these conditions the standard maximum likelihood asymptotic theory applies: if ω = ( γ$_{1}$, . . . , γ$_{p}$, λ 1 , . . . , λ$_{q}$ ) $_{, then}$√$_{I}$$_{(}$$_{ˆ}$ ω − ω ) D −→ N ( 0 , F − $^{1}$) , where F = E[ { ∂ ∂ ω log f ( y$_{i}$$_{·}$ ) } { ∂ ∂ ω log f ( y$_{i}$$_{·}$ ) } $^{T}$] is the Fisher Information Matrix for the parameter ω . Straightforward differentiation of (12), which is carried out in detail in Web Appendix B, gives 

and 

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Let ̂ u 2 ik = E( u 2 ik | y$_{i}$$_{·}$ ) and ̂ v 2 ijk = E( v 2 ijk | y$_{i}$$_{·}$ ) . Since E( ̂ u 2 $_{ik}$) = E( u 2 $_{ik}$) = γ$_{k}$ and E( ̂ v 2 $_{ijk}$) = E( v 2 $_{ijk}$) = λ$_{k}$ , we obtain the following expressions: 

and 

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The estimator ˆ F is obtained replacing expectations by averages over i = 1 , . . . , I . 

The asymptotic distribution of (9) is derived via the Delta Method: since h$_{z}$ is a differ- 

