Table 2.

Result of simulation experiment 2 under the linear model. Scaling weight parameter is set at s = 0 and s = 1. Tuning parameters are reported in log scale, i.e., λ̃ = log₁₀(λ+1). Sparsity denotes the proportion of zero in estimation. Results are summarized from 1,000 replications.

Method (λ̃opt)	β	β̂ size	λ̃Fuse,j	Sensitivity	Specificity	Sparsity	MSE when λ =
							λopt	λFuse	0
s = 0 BIC (0.143)	β1	8.115	1.517	0.373	0.995	0.199	0.006	0.063	0.014
	β2	10.689	1.551	0.401	0.996	0.166	0.008	0.150	0.014
	β3	13.107	1.962	0.443	0.997	0.113	0.008	0.313	0.014
	β4	15.178	1.984	0.461	0.997	0.091	0.009	0.500	0.014
	β5	4.818	0.301	0.322	–	0.338	0.003	0.000	0.014
	β6	4.860	0.305	0.322	–	0.339	0.003	0.000	0.014
	β7	4.860	0.302	0.321	–	0.330	0.003	0.000	0.014
	β8	4.820	0.301	0.319	–	0.336	0.003	0.000	0.014
s = 0 EBIC (0.159)	β1	7.538	1.509	0.417	0.994	0.224	0.006	0.063	0.014
	β2	9.975	1.546	0.441	0.995	0.182	0.007	0.150	0.014
	β3	12.212	1.953	0.483	0.996	0.124	0.008	0.313	0.014
	β4	14.096	1.978	0.503	0.997	0.101	0.008	0.500	0.014
	β5	4.388	0.298	0.377	–	0.394	0.003	0.000	0.014
	β6	4.413	0.303	0.379	–	0.397	0.003	0.000	0.014
	β7	4.408	0.299	0.374	–	0.385	0.003	0.000	0.014
	β8	4.385	0.298	0.374	–	0.392	0.003	0.000	0.014
s = 1 EBIC (0.492)	β1	3.563	1.589	0.800	0.981	0.422	0.006	0.063	0.014
	β2	6.111	1.810	0.708	0.989	0.291	0.007	0.150	0.014
	β3	8.843	2.388	0.667	0.994	0.168	0.007	0.313	0.014
	β4	11.358	2.521	0.635	0.995	0.128	0.008	0.500	0.014
	β5	1.329	0.275	0.928	–	0.932	0.000	0.000	0.014
	β6	1.321	0.280	0.933	–	0.937	0.000	0.000	0.014
	β7	1.311	0.274	0.934	–	0.935	0.000	0.000	0.014
	β8	1.321	0.273	0.929	–	0.936	0.000	0.000	0.014
MSE from K-means
K-means GCV (GDF)	β1	7.196	–	0.753	0.971	0.000			0.008
	β2	11.236	–	0.671	0.983	0.000			0.009
	β3	13.721	–	0.639	0.984	0.000			0.011
	β4	18.308	–	0.527	0.985	0.000			0.014
	β5	6.415	–	0.759	–	0.000			0.004
	β6	5.271	–	0.769	–	0.000			0.004
	β7	5.629	–	0.767	–	0.000			0.004
	β8	5.080	–	0.794	–	0.000			0.004