In many situations the values that a random variable, X, can take on is restricted, for example precipitation data [0,inf), that is f(x) = 0 for x < 0. We say that the support of f(x) is [0,inf).
We need to be modified when f(x) has bounded support. The simplest method of solving this problem is use a log transformation.
The idea is to estimate the probability density function (PDF) of a transformed random variable Y = t(X) which has unbounded support.
I have carries out the following steps:
(a) Transform the observations yi = t(xi), i = 1, 2,... , n.
(b) Apply the kernel method to estimate the PDF g(y) that is the density of Y = log(X).
My problem is how to estimate f(x) via kernel smooth of log(values) by using R?