Table 4.

Performance of JEAIC and JEBIC compared with JLIC for scenarios with Gaussian outcomes

Setups	Method	C(ρ)	1	2	3	4	5	6	7	8	9	10	Total
m = 0.1	JLIC	AR1	0	0	0	0	0	0	0.082	0	0.027	0.012	0.121
		EXC	0	0	0	0	0	0	0.654	0	0.141	0.083	0.878
		IND	0	0	0	0	0	0	0	0	0	0.001	0.001
		Total	0	0	0	0	0	0	0.736	0	0.168	0.096	1
	JEAIC	AR1	0	0	0	0	0	0	0.002	0	0	0	0.002
		EXC	0	0	0	0	0	0	0.802	0	0.124	0.072	0.998
		IND	0	0	0	0	0	0	0	0	0	0	0
		Total	0	0	0	0	0	0	0.804	0	0.124	0.072	1
	JEBIC	AR1	0	0	0	0	0	0	0.002	0	0	0	0.002
		EXC	0	0	0	0	0	0	0.991	0	0.005	0.002	0.998
		IND	0	0	0	0	0	0	0	0	0	0	0
		Total	0	0	0	0	0	0	0.993	0	0.005	0.002	1
m = 0.2	JLIC	AR1	0	0	0	0	0	0	0.163	0	0.034	0.027	0.224
		EXC	0	0	0	0.001	0	0	0.542	0.002	0.136	0.091	0.772
		IND	0	0	0	0	0	0	0	0	0.001	0.003	0.004
		Total	0	0	0	0.001	0	0	0.705	0.002	0.171	0.121	1
	JEAIC	AR1	0	0	0	0	0	0	0.01	0	0.006	0.001	0.017
		EXC	0	0	0	0	0	0	0.744	0	0.156	0.083	0.983
		IND	0	0	0	0	0	0	0	0	0	0	0
		Total	0	0	0	0	0	0	0.754	0	0.162	0.084	1
	JEBIC	AR1	0	0	0	0	0	0	0.016	0	0.001	0	0.017
		EXC	0	0	0	0	0	0	0.975	0	0.007	0.001	0.983
		IND	0	0	0	0	0	0	0	0	0	0	0
		Total	0	0	0	0	0	0	0.991	0	0.008	0.001	1

The sample size n = 500, T = 3, ρ = 0.3 across 1,000 Monte Carlo datasets. Ten candidate models are considered: {1} = {x₁}, {2} = {x₂}, {3} = {x₁, x₂}, {4} = {x₁, x₃}, {5} = {x₁, x₃, x_1,3}, {6} = {x₁, x₂, x_1,2}, {7} = {x₁, x₂, x₃}, {8} = {x₂, x₃, x_2,3}, {9} = {x₁, x₂, x₃, x_1,2, x_1,3}, {10} = {x₁, x₂, x₃, x_1,2, x_1,3, x_1,3}. It is noted that Model {7} = {x₁, x₂, x₃} with an EXC correlation structure is the true model. The variable x₄ is redundant.

Abbreviations: EXC, exchangeable; IND, independence; JEAIC, joint empirical Akaike information criterion; JEBIC, joint empirical Bayesian information criterion; JLIC, joint longitudinal information criterion; MLIC, missing longitudinal information criterion; QICW, weighted quasi-likelihood information criterion.

The bold values denote the true mean and correlation structures.