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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 ...

Estimation of the value of a density function at a point of continuity using a kernel-type estimator is discussed and improvements of the technique are presented. The generalized jackknife method is ...

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

We consider the Parzen-Rosenblatt kernel density estimate on Rd with data-dependent smoothing factor. Sufficient conditions on the asymptotic behavior of the smoothing factor are given under which the ...

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 ...

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 ...