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, ...

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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 ...

The performance of a kernel density estimator depends crucially on the size of its smoothing bandwidth. A data-driven bandwidth selector for density estimation at a point is proposed in this paper.

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

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 ...