Table 1:

(Low-dimensional settings: n = 100, p ∈ {10, 50, 100} Comparison of BAR, Lasso, SCAD, MCP, and Adaptive Lasso (ALasso) and TLP based on 100 Monte-Carlo samples. (Misclassification = mean number of misclassified non-zeros and zeros; FP = mean number of false positives (non-zeros); FN = mean number of false negatives (zeros); TM = probability that the selected model is exactly the true model; MAB = mean absolute bias; MSPE = mean squared prediction error from five-fold CV.)

Model	p	Method	Misclassification	FP	FN	TM	MAB	MSPE
1	10	BAR	0.08	0.08	0	93%	0.26	1.02
		Lasso	3.28	3.28	0	6%	0.56	1.08
		SCAD	0.90	0.90	0	62%	0.34	1.03
		MCP	0.72	0.72	0	68%	0.33	1.03
		ALasso	0.26	0.26	0	89%	0.29	1.03
		TLP(0.15)	0.54	0.54	0	63%	0.34	1.04
		TLP(0.5)	0.64	0.64	0	77%	0.31	1.04
	50	BAR	0.26	0.26	0	86%	0.32	1.04
		Lasso	7.75	7.75	0	2%	0.95	1.18
		SCAD	1.81	1.81	0	65%	0.37	1.05
		MCP	0.97	0.97	0	73%	0.34	1.05
		ALasso	3.99	3.99	0	40%	0.68	1.03
		TLP(0.15)	0.98	0.98	0	60%	0.45	1.05
		TLP(0.5)	1.51	1.51	0	77%	0.36	1.08
	100	BAR	0.44	0.44	0	80%	0.35	1.02
		Lasso	10.38	10.38	0	2%	1.08	1.2
		SCAD	1.41	1.41	0	65%	0.31	1.03
		MCP	1.04	1.04	0	66%	0.32	1.03
		ALasso	1.51	1.51	0	32%	0.42	1.08
		TLP(0.15)	1.31	1.31	0	55%	0.46	1.02
		TLP(0.5)	2.21	2.21	0	70%	0.38	1.05
2	10	BAR	1.14	0.12	1.02	30%	0.67	1.11
		Lasso	2.70	2.61	0.09	5%	0.76	1.11
		SCAD	1.87	1.58	0.29	14%	0.74	1.1
		MCP	1.69	1.27	0.42	19%	0.74	1.1
		ALasso	1.55	1.11	0.44	18%	0.69	1.07
		TLP(0.15)	1.02	0.40	0.62	33%	0.66	1.07
		TLP(0.5)	2.27	1.87	0.4	9%	0.74	1.09
	50	BAR	2.18	0.68	1.5	4%	0.94	1.17
		Lasso	10.56	10.09	0.47	0%	1.51	1.29
		SCAD	5.36	4.32	1.04	1%	1.06	1.19
		MCP	3.33	2.1	1.23	4%	0.99	1.19
		ALasso	8.22	7.38	0.84	0%	1.41	1.1
		TLP(0.15)	2.25	1.36	1.19	7%	1.03	1.1
		TLP(0.5)	6.07	5.13	0.94	3%	1.09	1.2
	100	BAR	2.67	0.94	1.73	2%	1.05	1.17
		Lasso	14.63	14.02	0.61	0%	1.79	1.34
		SCAD	4.24	3.08	1.16	1%	1.01	1.21
		MCP	3.34	1.99	1.35	3%	0.96	1.2
		ALasso	4.42	2.27	2.15	1%	1.26	1.32
		TLP(0.15)	3.14	1.88	1.26	2%	1.03	1.08
		TLP(0.5)	7.67	6.71	0.96	1%	1.17	1.19