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 ...
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 ...
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 ...
CATALOG DESCRIPTION: Fundamental and advanced topics in statistical pattern recognition including Bayesian decision theory, Maximum-likelihood and Bayesian estimation, Nonparametric density estimation ...
Nonparametric estimation under shape constraints represents a vibrant field that bridges rigorous mathematical theory with practical applications. This approach leverages inherent qualitative ...
After publication of an earlier version of this paper, we received feedback that there were several incorrect references to related methods in the literature. These errors are corrected in the current ...
CATALOG DESCRIPTION: Fundamental and advanced topics in statistical pattern recognition including Bayesian decision theory, Maximum-likelihood and Bayesian estimation, Nonparametric density estimation ...
The KDE procedure performs either univariate or bivariate kernel density estimation. Statistical density estimation involves approximating a hypothesized probability density function from observed ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results