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 ...

Refer to Silverman (1986) or Scott (1992) for an introduction to nonparametric density estimation. PROC MODECLUS uses (hyper)spherical uniform kernels of fixed or variable radius. The density estimate ...

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 ...

Continuity or discontinuity of probability density functions of data often plays a fundamental role in empirical economic analysis. For example, for identification and inference of causal effects in ...

A dual-model battery health assessment framework analyzes real-world voltage data from retired EV batteries in grid storage. Using incremental capacity and probability density methods, it improves ...