Nothing
cutoff.edgeworth<-function(xin, dist, kfun, p1, p2, sig.lev )
{
n<-length(xin)
h<-bw.nrd(xin) #pilot bandwidth for the kernel estimate
DensityEst <- kde(xin,xin, h, Gaussian)
Rhatfx <- mean(DensityEst) #estimate R(f)
RhatfxUse<-Rhatfx^{3/2}
RK<- 3/5 # for epanechnikov, 1/(2*sqrt(pi)) #for gaussian, 1/2 # for uniform,
RKuse<-RK^{3/2}
ConvK<- switch(kfun,
Gaussian = 0.2765382,
Epanechnikov = 0.409542,
Triangular = 0.3703704,
Rectangular = 0.3703704,
Biweight = 0.3276294,
Epanechnikov2 = 0.409542
) #kernel.conv(kernel.conv(0, deriv.order = 0, kernel = kfun)$kx, deriv.order = 0, kernel = kfun)$kx
ML.Dens<- NDistDens(xin, dist, p1, p2)
mu.2 <- mean((DensityEst - ML.Dens)^2)
nu.2 <- mean(DensityEst^2)
SigmaSq <- 2* mu.2^2 * nu.2 * RK
banduse<-hopt.edgeworth(xin, dist, kfun, p1, p2, sig.lev )
RDeltaR<- mean( (ML.Dens - DensityEst)^2 * DensityEst )
z.a<-qnorm(mean=mean(xin), sd=sd(xin), 1-sig.lev/2)
n<-length(xin)
sqrt.h<-sqrt(banduse)
z.a.sq<-z.a^2 -1
d0.1<- z.a.sq * ConvK * mean(DensityEst^3) *mu.2^3 / (3*SigmaSq^(3/2) )
d0.2 <- RDeltaR /sqrt(2*nu.2 * RK)
d0<- d0.1-d0.2
d2<- z.a.sq * mean((DensityEst - ML.Dens)^3)^2 * kfun(0)^2 / SigmaSq^(3/2)
cut.off<- z.a + d0 * sqrt.h + d2/( n*sqrt.h)
cut.off
}
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