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 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 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- 12 F$_{p}$ + k,p + l = − J 2 4 λ$_{k}$λ$_{l}$ + E( ∑ J j =1 ̂ v 2 ijk ∑ J j =1 ̂ v 2 $_{ijl}$) 4 λ 2 $_{k}$λ 2 l , for k = 1 , . . . q and l = 1 , . . . , q. F$_{k,p}$ + l = − J 4 γ$_{k}$λ$_{l}$ + E( ̂ u 2 ik ∑ J j =1 ̂ v 2 $_{ijl}$) 4 γ 2 $_{k}$λ 2 l , for k = 1 , . . . , p and l = 1 , . . . , q, F$_{kl}$ = − 1 4 γ$_{k}$γ$_{l}$ + E( ̂ u 2 ik ̂ u 2 $_{il}$) 4 γ 2 $_{k}$γ 2 l , for k = 1 , . . . , p and l = 1 , . . . , p, ∂ ∂λ$_{k}$ log f ( y$_{i}$$_{·}$ ) = − J 2 λ$_{k}$ + 1 2 λ 2 k J ∑ j =1 E( v 2 ijk | y$_{i}$$_{·}$ ) , k = 1 , . . . , q. ∂ ∂γ$_{k}$ log f ( y$_{i}$$_{·}$ ) = − 1 2 γ$_{k}$ + E( u 2 ik | y$_{i}$$_{·}$ ) 2 γ 2 k , k = 1 , . . . , p,