Is there any particular reason you will choose the kernel density estimation over the parametric estimation? I was learning to fit distribution to my data. This question came to me.
My data size is relavtively large with 7500 data points. Auto claims. My goal is to fit it to a distribution(nonparametric or parametric). And then use it to simulate auto claim data, and calculate VaR or TVaR.
I used log to transform the data to make it relatively normal. I fitted many distributions including normal, lognormal, gamma, t,etc...I used AIC and loglikehood to identify the best fitting. But none of all this fitting passed KS test(p value extremmly small, with e-10).
That's why I asked in what situation I should switch to KDE.