Kernel Density Estimation: Non-parametrically estimating the probability density function from data using kernel functions, enabling flexible adaptation to complex distributions.

We propose a method for reconstructing a probability density function (pdf) from a sample of an n -dimensional probability distribution. The method works by iteratively applying simple transformations ...

Building on the widely-used double-lognormal approach by Bahra (1997), this paper presents a multi-lognormal approach with restrictions to extract risk-neutral probability density functions (RNPs) for ...

Nonparametric estimation of probability density functions, both marginal and joint densities, is a very useful tool in statistics. The kernel method is popular and applicable to dependent data, ...

In some situations only the statistical properties of such objects are desired: the three-dimensional probability density function. This article demonstrates that under special symmetries this ...