Table 8.

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

Fixed Bandwidth h = 0.2
Sample size	MSE	BIAS
n = [75,75]	[0.09389, 0.08542]	[0.30641, 0.29226]
n = [65,65]	[0.05138, 0.03421]	[0.22667, 0.18495]
n = [55,55]	[0.07563, 0.06691]	[0.27500, 0.25866]
n = [45,45]	[0.05114, 0.05046]	[0.22614, 0.22463]
n = [35,35]	[0.05156, 0.04487]	[0.22706, 0.21182]
Fixed Bandwidth h = 0.5
n = [75,75]	[0.09544, 0.08645]	[0.30893, 0.29402]
n = [65,65]	[0.05509, 0.04258]	[0.23471, 0.20634]
n = [55,55]	[0.08372, 0.06666]	[0.28934, 0.25818]
n = [45,45]	[0.05292, 0.04750]	[0.23004, 0.21794]
n = [35,35]	[0.05530, 0.04453]	[0.23515, 0.21102]
${\mathit{h}}_{\mathit{w}\mathit{j}\mathbf{1}}$: Direct Plug-in Method, Wand and Jones [15]
n = [75,75]	[0.08938, 0.08278]	[0.29896, 0.28771]
n = [65,65]	[0.04581, 0.03422]	[0.21403, 0.18498]
n = [55,55]	[0.07358, 0.06531]	[0.27125, 0.25555]
n = [45,45]	[0.05838, 0.04687]	[0.24161, 0.21649]
n = [35,35]	[0.04910, 0.04319]	[0.22158, 0.20782]
${\mathit{h}}_{\mathit{w}\mathit{j}\mathbf{2}}$: Direct Plug-in Method, Wand and Jones [15]
n = [75,75]	[0.08962, 0.08276]	[0.29936, 0.28768]
n = [65,65]	[0.04539, 0.03403]	[0.21304, 0.18447]
n = [55,55]	[0.07361, 0.06530]	[0.27131, 0.25553]
n = [45,45]	[0.05850, 0.04674]	[0.24186, 0.21619]
n = [35,35]	[0.04924, 0.04316]	[0.22190, 0.20774]
${\mathit{h}}_{\mathit{D}\mathit{S}\mathbf{1}}$: Biased Cross-validation, Scott and Terrell [16]
n = [75,75]	[0.09067, 0.08417]	[0.30111, 0.29012]
n = [65,65]	[0.04522, 0.03427]	[0.21264, 0.18512]
n = [55,55]	[0.07464, 0.06670]	[0.27320, 0.25826]
n = [45,45]	[0.05811, 0.04830]	[0.24106, 0.21977]
n = [35,35]	[0.05031, 0.04484]	[0.22429, 0.21175]
${\mathit{h}}_{\mathit{D}\mathit{S}\mathbf{1}}$: Biased Cross-validation, Scott and Terrell [16]
n = [75,75]	[0.09065, 0.08334]	[0.30108, 0.28868]
n = [65,65]	[0.04512, 0.03415]	[0.21241, 0.18479]
n = [55,55]	[0.07448, 0.06627]	[0.27291, 0.25742]
n = [45,45]	[0.05829, 0.04800]	[0.24143, 0.21908]
n = [35,35]	[0.05008, 0.04439]	[0.22378, 0.21068]