Table 2.

Average Model Weights, Average Rank Order, and Number of Rank Order Ones (i.e., Wins) for ARMA and ARFIMA Models Fitted to Van Orden, Holden, and Turvey’s (2003) Experiment 2 (i.e., Word Naming), Separately for AIC and BIC

	AIC				BIC
	Weight				Weight
Model	M	SD	Rank	Wins	M	SD	Rank	Wins
Transient correlation
ARMA (0, 0)	.00	.00	18.0	0	.00	.00	16.8	0
ARMA (1, 0)	.04	.06	10.9	2	.21	.24	5.4	6
ARMA (2, 0)	.04	.05	8.9	0	.03	.05	7.7	0
ARMA (0, 1)	.02	.04	14.3	0	.09	.13	8.9	0
ARMA (0, 2)	.03	.06	11.4	2	.03	.10	9.5	1
ARMA (1, 1)	.06	.06	7.3	1	.04	.08	5.1	0
ARMA (2, 1)	.06	.05	7.3	0	.00	.00	9.3	0
ARMA (1, 2)	.06	.04	7.1	1	.00	.00	8.8	0
ARMA (2, 2)	.03	.02	11.3	0	.00	.00	14.9	0
Persistent correlation
ARFIMA (0, d, 0)	.07	.08	8.0	3	.47	.35	2.5	12
ARFIMA (1, d, 0)	.06	.03	6.4	0	.03	.02	4.5	0
ARFIMA (2, d, 0)	.03	.02	10.3	0	.00	.00	10.9	0
ARFIMA (0, d, 1)	.05	.03	7.1	0	.03	.02	5.2	0
ARFIMA (0, d, 2)	.04	.04	10.8	0	.00	.01	11.0	0
ARFIMA (1, d, 1)	.06	.08	9.1	1	.04	.18	9.4	1
ARFIMA (2, d, 1)	.14	.14	7.0	7	.00	.01	12.3	0
ARFIMA (1, d, 2)	.09	.12	9.3	1	.00	.02	13.2	0
ARFIMA (2, d, 2)	.11	.15	6.9	2	.00	.00	16.1	0

Note. Weight denotes the average model weights (i.e., averaged over participants). Rank denotes the average rank order of the models (i.e., averaged over participants). The ARMA and ARFIMA models that are best in terms of average weight, average rank order, and total number of wins are in bold. AIC = Akaike’s information criterion; BIC = Bayesian information criterion; ARMA = autoregressive moving average; ARFIMA = autoregressive fractionally integrated moving average.