# Kernel density estimation on bounded support

I was looking for some way to deal with boundary bias of kde in case of unit interval. One example is an usage of Chen estimators (or Beta estimators; an example might be seen here: http://stats-www.open.ac.uk/TechnicalReports/mcjdah.pdf -p.4) - instead of typical kernel density estimator $$\hat{f}(x) =\frac{1}{n} \sum_{i=1}^{n}K(x,X_{i};h)$$ we obtain:

$$\hat{f_{C1}}(x)=\frac{1}{nB(\frac{x}{h^2}+1,\frac{1-x}{h^2}+1)}\sum_{i=1}^{n}X_{i}^{x / h^2}(1-X_{i})^{(1-x) / h^2},$$ where B() is beta function

The difficulty, which I encountered are underflow problems in calculating beta function in case of large values of parameters. For example in R:

data <- runif(10000)

Chen_kde <- function(x,input,h=1/length(input)^(0.9)){

p = x / h + 1
q = (1-x) / h + 1

output = mean(trans_data^(p-1)*(1-trans_data)^(q-1)/beta(p,q))
return(output)
}

Chen_kde(0.1,data)

Warning message:
In beta(p, q) : underflow occurred in 'beta'


I found that one way to tackle this problem is to approximate beta distribution with normal density with equal mean and std deviation. However, each element of above mentioned sum is only "simmilar" to beta distribution since x lies in exponent, not in base. My question is if in this example I can also approximate somehow each element to get rid of underflow problems or there can be some other succesful methods to correct boundary bias of kde for unit interval.