Table 2.

Estimated bias and variance of β̂ for each sampling method. For β̂ ∈ ℝ^p, we define Bias² = ‖𝔼β̂ − β‖² and $\text{Var}={\sum }_{j=1}^p\text{Var}({\mathrm{\beta ̂}}_j)$

Simulation 1 (Σ0 ≠ Σ1 ⇒ model misspecified)
	${\widehat{\text{Bias}}}^2$	(s.e.)	$\widehat{\text{Var}}$	(s.e.)
LCC	0.0049	(0.00031)	0.025	(0.00059)
WCC	0.023	(0.0022)	0.16	(0.0038)
CC	0.15	(0.0016)	0.043	(0.00096)
Simulation 2 (Σ0 = Σ1 ⇒ model correct)
	${\widehat{\text{Bias}}}^2$	(s.e.)	$\widehat{\text{Var}}$	(s.e.)
LCC	0.0037	(0.0083)	0.039	(0.00045)
WCC	0.59	(0.064)	1.7	(0.017)
CC	0.06	(0.042)	0.87	(0.0086)