Results 21 to 30 of about 23,658 (304)

Nonparametric Mean Estimation for Big-but-Biased Data

Proceedings, 2018
Some authors have recently warned about the risks of the sentence with enough data, the numbers speak for themselves. The problem of nonparametric statistical inference in big data under the presence of sampling bias is considered in this work.
Laura Borrajo, Ricardo Cao
doaj   +1 more source

Nonparametric Multivariate Density Estimation: Case Study of Cauchy Mixture Model

Mathematics, 2021
Estimation of probability density functions (pdf) is considered an essential part of statistical modelling. Heteroskedasticity and outliers are the problems that make data analysis harder. The Cauchy mixture model helps us to cover both of them.
Tomas Ruzgas, Mantas Lukauskas, Gedmantas Čepkauskas   +2 more
doaj   +1 more source

Conditional density estimation with class probability estimators [PDF]

, 2009
Many regression schemes deliver a point estimate only, but often it is useful or even essential to quantify the uncertainty inherent in a prediction. If a conditional density estimate is available, then prediction intervals can be derived from it.
Remco R. Bouckaert, Eibe Frank, Frank, Eibe, Bouckaert, Remco R.   +3 more
core   +1 more source

Clustering via Nonparametric Density Estimation: The R Package pdfCluster

Journal of Statistical Software, 2014
The R package pdfCluster performs cluster analysis based on a nonparametric estimate of the density of the observed variables. Functions are provided to encompass the whole process of clustering, from kernel density estimation, to clustering itself and ...
Adelchi Azzalini, Giovanna Menardi
doaj   +1 more source

Quasar Identification Using Multivariate Probability Density Estimated from Nonparametric Conditional Probabilities

Mathematics, 2022
Nonparametric estimation for a probability density function that describes multivariate data has typically been addressed by kernel density estimation (KDE).
Jenny Farmer, Eve Allen, Donald J. Jacobs   +2 more
doaj   +1 more source

Using conditional kernel density estimation for wind power density forecasting [PDF]

, 2012
Of the various renewable energy resources, wind power is widely recognized as one of the most promising. The management of wind farms and electricity systems can benefit greatly from the availability of estimates of the probability distribution of wind ...
Jeon, Jooyoung, Taylor, James W., Jeon, Jooyong, Taylor, James   +3 more
core   +1 more source

Nonparametric Copula Density Estimation Methodologies

Mathematics
This paper proposes several methodologies whose objective consists of securing copula density estimates. More specifically, this aim will be achieved by differentiating bivariate least-squares polynomials fitted to Deheuvels’ empirical copulas, by making
Serge B. Provost, Yishan Zang
doaj   +1 more source

Fast Nonparametric Conditional Density Estimation

CoRR, 2012
Appears in Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence (UAI2007)
Michael P. Holmes, Alexander G. Gray, Charles L. Isbell Jr.   +2 more
openaire   +3 more sources

Radar Target Recognition Based on Semiparametric Density Estimation of SLC

Leida xuebao, 2012
In order to solve the problem of the decline of accuracy when using the nonparametric method—Stochastic Learning of the Cumulative (SLC) to estimate the density of High-Resolution Range Profile (HRRP) in radar target recognition under the condition that ...
Cui Shan-shan, Zhou Jian-jiang, Zhu Jie-hao   +2 more
doaj   +1 more source

Nonparametric Kernel Density Estimation Near the Boundary [PDF]

SSRN Electronic Journal, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Peter Malec, Melanie Schienle
openaire   +4 more sources