Description Usage Arguments Value Examples
Basic computing engine called by LPPOptBW
used to find
optimal bandwidth
1 2  LPPOptBW.fit(d, M, kern = "triangular", opt.criterion, alpha = 0.05,
beta = 0.8, sclass = "H", order = 1, se.initial = "ROTEHW")

d 
object of class 
M 
Bound on second derivative of the conditional mean function. 
kern 
specifies kernel function used in the local regression. It can
either be a string equal to 
opt.criterion 
Optimality criterion that bandwidth is designed to optimize. It can either be based on exact finitesample maximum bias and finitesample estimate of variance, or asymptotic approximations to the bias and variance. The options are:
The finitesample methods use conditional variance given by

alpha 
determines confidence level, 
beta 
Determines quantile of excess length to optimize, if bandwidth optimizes given quantile of excess length of onesided confidence intervals. 
sclass 
Smoothness class, either 
order 
Order of local regression 1 for linear, 2 for quadratic. 
se.initial 
Method for estimating initial variance for computing optimal bandwidth. Ignored if data already contains estimate of variance.

a list with the following elements
h
Bandwidth
sigma2
estimate of conditional variance, from d
1 2 3  # Lee dataset
d < LPPData(lee08[lee08$margin>0, ], point=0)
LPPOptBW.fit(d, kern = "uniform", M = 0.1, opt.criterion = "MSE")$h

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