The kernel method is a nonparametric procedure used to estimate densities with support in
R. When nonnegative data are modeled, the classical kernel density estimator presents a …

Kernel estimation of a probability density function supported on the unit interval has proved
difficult, because of the well-known boundary bias issues a conventional kernel density …

Abstracts Probabilistic wind power forecasting not only produces the expectation of wind
power output, but also gives quantitative information on the associated uncertainty, which is …

For density and distribution functions supported on [0, 1], Bernstein polynomial estimators
are known to have optimal mean integrated squared error (MISE) properties under the usual …

In some applications of kernel density estimation the data may have a highly non-uniform
distribution and be confined to a compact region. Standard fixed bandwidth density …

The beta kernel estimators are shown in Chen [SX Chen, Beta kernel estimators for density
functions, Comput. Statist. Data Anal. 31 (1999), pp. 131–145] to be non-negative and have …

Abstract The Receiver Operating Characteristic (ROC) curve is a statistical tool for
evaluating the accuracy of diagnostics tests. The empirical ROC curve (which is a step …

Аннотация Предложена новая методика оценки надежности небольших
вычислительных систем, позволяющих сформировать только малые выборки …

The kernel density estimator (KDE) suffers boundary biases when applied to densities on
bounded supports, which are assumed to be the unit interval. Transformations map** the …

Abstract The Aw-Rascle-Zhang (ARZ) model has become a favorable''second order"
macroscopic traffic model, which corrects several shortcomings of the Payne-Whitham (PW) …