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, ...

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 cumulative distribution function gives the proportion of the data less than each possible value. A density function is the derivative of the cumulative distribution function. Density estimation is ...

We study a nonparametric deconvolution density estimation problem. The estimator is obtained by an EM algorithm for a smoothed maximum likelihood estimation problem, which has a unique continuous ...