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 ...

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

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, ...

A kernel density curve may follow the shape of the distribution more closely. To construct a normal kernel density curve, one parameter is required: the bandwidth .The value of determines the degree ...

We introduce a consistent estimator for the homology (an algebraic structure representing connected components and cycles) of level sets of both density and regression functions. Our method is based ...

Gordon Lee et al introduce a data-driven and model-agnostic approach for computing conditional expectations. The new method combines classical techniques with machine learning methods, in particular ...