Density estimation is a fundamental component in statistical analysis, aiming to infer the probability distribution of a random variable from a finite sample without imposing restrictive parametric ...

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 ...

The KDE procedure performs either univariate or bivariate kernel density estimation. Statistical density estimation involves approximating a hypothesized probability density function from observed ...

Several studies have predicted that not all geomagnetic reversals have been discovered, but it was unknown in which periods they might be hidden. Researchers led by the National Institute of Polar ...

A dual-model battery health assessment framework analyzes real-world voltage data from retired EV batteries in grid storage. Using incremental ...

All methods for estimating the risk-neutral density from the volatility smile boil down to the completion of the implied volatility function by interpolating between available strike prices and ...