Table 6.

Mean Square Error and Bias for the ${\bar{y}}_{MBN}$ using Jump data set.

Fixed Bandwidth h = 0.2
Sample size	MSE	BIAS
n = [100,100]	[0.01205009, 0.01188809]	[0.02199, 0.02232]
n = [150,150]	[0.01658871, 0.01798214]	[0.02319, 0.02401]
n = [200,200]	[0.02289497, 0.02191454]	[0.02492, 0.02523]
n = [250,250]	[0.02546805, 0.02469677]	[0.02588, 0.02632]
n = [300,300]	[0.02863773, 0.02848747]	[0.02702, 0.02773]
Fixed Bandwidth h = 0.5
n = [100,100]	[0.01214137, 0.01198277]	[0.02212, 0.01281]
n = [150,150]	[0.01668117, 0.01807667]	[0.02327, 0.02431]
n = [200,200]	[0.02298611, 0.02200692]	[0.02517, 0.02579]
n = [250,250]	[0.02554803, 0.02477629]	[0.02593, 0.02641]
n = [300,300]	[0.02871328, 0.02856456]	[0.02719, 0.02781]
${\mathit{h}}_{\mathit{w}\mathit{j}\mathbf{1}}$: Direct Plug-in Method, Wand and Jones [15]
n = [100,100]	[0.01206712, 0.01187864]	[0.02217, 0.01293]
n = [150,150]	[0.01659800, 0.01791894]	[0.02391, 0.02467]
n = [200,200]	[0.02288552, 0.02191070]	[0.02563, 0.02592]
n = [250,250]	[0.02546793, 0.02469395]	[0.02603, 0.02681]
n = [300,300]	[0.02863886, 0.02848247]	[0.02728, 0.02795]
${\mathit{h}}_{\mathit{w}\mathit{j}\mathbf{2}}$: Direct Plug-in Method, Wand and Jones [15]
n = [100,100]	[0.01205550, 0.01188799]	[0.02242, 0.01317]
n = [150,150]	[0.01659424, 0.01792550]	[0.02477, 0.02489]
n = [200,200]	[0.02289183, 0.02191304]	[0.02532, 0.02594]
n = [250,250]	[0.02546676, 0.02469463]	[0.02611, 0.02683]
n = [300,300]	[0.02863664, 0.02848310]	[0.02732, 0.02798]
${\mathit{h}}_{\mathit{D}\mathit{S}\mathbf{1}}$: Biased Cross-validation, Scott and Terrell [16]
n = [100,100]	[0.01213442, 0.01197712]	[0.02251, 0.01352]
n = [150,150]	[0.01666418, 0.01800725]	[0.02481, 0.02494]
n = [200,200]	[0.02296544, 0.02200771]	[0.02539, 0.02603]
n = [250,250]	[0.02552291, 0.02479956]	[0.02618, 0.02686]
n = [300,300]	[0.02868704, 0.02857982]	[0.02741, 0.02807]
${\mathit{h}}_{\mathit{D}\mathit{S}\mathbf{2}}$: Biased Cross-validation, Scott and Terrell [16]
n = [100,100]	[0.01217254, 0.01199764]	[0.02258, 0.01361]
n = [150,150]	[0.01671417, 0.01803506]	[0.02488, 0.02505]
n = [200,200]	[0.02301658, 0.02196197]	[0.02543, 0.02610]
n = [250,250]	[0.02554021, 0.02472381]	[0.02621, 0.02691]
n = [300,300]	[0.02867209, 0.02851481]	[0.02755, 0.02813]