The KDE procedure performs either univariate or bivariate kernel density estimation. Statistical density estimation involves approximating a hypothesized probability density function from observed ...
Nonparametric estimation under shape constraints represents a vibrant field that bridges rigorous mathematical theory with practical applications. This approach leverages inherent qualitative ...
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 ...
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 ...
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 ...
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