Nonparametric methods provide a flexible framework for estimating the probability density function of random variables without imposing a strict parametric model. By relying directly on observed data, ...

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 some simple ...

This is a preview. Log in through your library . Abstract We examine large-sample properties of cross-validation for estimating cell probabilities, starting from a completely general measure of loss.

Our eLibrary offers over 25,000 IMF publications in multiple formats. Building on the widely-used double-lognormal approach by Bahra (1997), this paper presents a multi-lognormal approach with ...

Will Kenton is an expert on the economy and investing laws and regulations. He previously held senior editorial roles at Investopedia and Kapitall Wire and holds a MA in Economics from The New School ...

Kernel density estimation (KDE) and nonparametric methods form a cornerstone of contemporary statistical analysis. Unlike parametric approaches that assume a specific functional form for the ...