Fig. 1.

Simulation results. Subplot (a): SNR = 1, 2, 3, with n = 200, p = 1000, q = 10, ${\alpha }_0=3,\hspace{0.17em}\delta =0.5,\hspace{0.17em}\rho =0.2$. Subplot (b): p = 500, 1000, 3000, with n = 200, SNR = 2, q = 10, ${\alpha }_0=3,\hspace{0.17em}\delta =0.5,\hspace{0.17em}\rho =0.2$. Subplot (c): q = 10, 30, 50, with n = 200, p = 1000, SNR = 2, ${\alpha }_0=3,\hspace{0.17em}\delta =0.5,\hspace{0.17em}\rho =0.2$. Subplot (d): regression coefficients sparsity δ = $0.3,0.5,0.7$, with n = 200, p = 1000, SNR = 2, q = 10, ${\alpha }_0=3,\hspace{0.17em}\rho =0.2$. Subplot (e): overall penalty magnitude α₀ = 1, 3, 5, n = 200, p = 1000, SNR = 2, q = 10, $\delta =0.5,\hspace{0.17em}\rho =0.2$. Subplot (f): $\rho =0.3,0.6,0.9$, with n = 200, p = 1000, SNR = 2, q = 10, ${\alpha }_0=3,\hspace{0.17em}\delta =0.5$