TABLE 3.

Performance of ELCIC under multiple robust estimators and WGEE estimators with an independent correlation structure: percentages of selecting six candidate models across 500 Monte Carlo data are summarized

Setups	Method	x 1	x1, x2, x3, x4, x5	x 2	x1, x2	x1, x2, x3	x1, x2, x4, x5
n = 400	WGEE_100	0.174	0	0.272	0.538	0.014	0.002
m = 0.3	WGEE_010	0.192	0	0.298	0.496	0.01	0.004
	MULTI_100	0.142	0	0.248	0.6	0.01	0
	MULTI_111	0.142	0	0.254	0.596	0.008	0
	MULTI_010	0.138	0	0.256	0.596	0.01	0
	MULTI_011	0.14	0	0.252	0.6	0.008	0
n = 700	WGEE_100	0.042	0	0.104	0.85	0.004	0
m = 0.3	WGEE_010	0.052	0	0.118	0.818	0.008	0.004
	MULTI_100	0.018	0	0.068	0.906	0.008	0
	MULTI_111	0.022	0	0.07	0.904	0.004	0
	MULTI_010	0.02	0	0.068	0.908	0.004	0
	MULTI_011	0.02	0	0.072	0.904	0.004	0
n = 400	WGEE_100	0.408	0	0.402	0.186	0	0.004
m = 0.5	WGEE_010	0.448	0	0.424	0.12	0.004	0.004
	MULTI_100	0.276	0	0.292	0.428	0.002	0.002
	MULTI_111	0.276	0	0.296	0.422	0.004	0.002
	MULTI_010	0.272	0	0.302	0.422	0.002	0.002
	MULTI_011	0.276	0	0.296	0.422	0.004	0.002
n = 700	WGEE_100	0.31	0.002	0.29	0.388	0.01	0
m = 0.5	WGEE_010	0.396	0.002	0.348	0.244	0.01	0
	MULTI_100	0.098	0.002	0.12	0.764	0.012	0.004
	MULTI_111	0.096	0.002	0.122	0.764	0.012	0.004
	MULTI_010	0.102	0.002	0.114	0.762	0.016	0.004
	MULTI_011	0.1	0.002	0.122	0.756	0.016	0.004

Notes. The model containing the covariates {x₁, x₂} is the true model.