Kernel approach estimating the variance via bootstrap in the logit scale and back-transformed.
Usage
OVL.LogitK(x, y, alpha = 0.05, B = 100, k = 1, h = 1)
Value
confidence interval.
Arguments
x
Numeric vector. Data from the first group.
y
Numeric vector. Data from the second group.
alpha
confidence level.
B
bootstrap size.
k
kernel. When k=1 (default value) the kernel used in the estimation is the Gaussian kernel. Otherwise, the Epanechnikov kernel is used instead.
h
bandwidth. When h=1 (default value) the cross-validation bandwidth is chosen. Otherwise, the bandwidth considered by Schmid and Schmidt (2006) is used instead.