Results 131 to 140 of about 257,461 (188)

Prediction model-based kernel density estimation when group membership is subject to missing. [PDF]

Adv Stat Anal, 2017
He H, Wang W, Tang W.
europepmc   +1 more source

Using kernel density estimation to understand the influence of neighbourhood destinations on BMI. [PDF]

BMJ Open, 2016
King TL, Bentley RJ, Thornton LE, Kavanagh AM.   +3 more
europepmc   +1 more source

Demonstration and validation of Kernel Density Estimation for spatial meta-analyses in cognitive neuroscience using simulated data. [PDF]

Data Brief, 2017
Belyk M, Brown S, Kotz SA.
europepmc   +1 more source

Kernel Density Estimation as a Measure of Environmental Exposure Related to Insulin Resistance in Breast Cancer Survivors. [PDF]

Cancer Epidemiol Biomarkers Prev, 2017
Jankowska MM, Natarajan L, Godbole S, Meseck K, Sears DD, Patterson RE, Kerr J.   +6 more
europepmc   +1 more source

The Use of Kernel Density Estimation With a Bio-Physical Model Provides a Method to Quantify Connectivity Among Salmon Farms: Spatial Planning and Management With Epidemiological Relevance. [PDF]

Front Vet Sci, 2018
Cantrell DL, Rees EE, Vanderstichel R, Grant J, Filgueira R, Revie CW.   +5 more
europepmc   +1 more source

Research on precise estimation of line loss rate probability density based on bilateral total variation filtering algorithm. [PDF]

Sci Rep
Zhang J, Liu S, Feng C.
europepmc   +1 more source

Photovoltaic power interval prediction with conditional error dependency using Bayesian optimized deep learning. [PDF]

Sci Rep
Chen Y, Wang X, Huang R, You G.
europepmc   +1 more source

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Robust kernels for kernel density estimation

Economics Letters, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wang, Shaoping, Li, Ang, Wen, Kuangyu, Wu, Ximing   +3 more
openaire   +2 more sources

On nonparametric kernel density estimates

Biometrika, 1990
SUMMARY The paper introduces the idea of inadmissible kernels and shows that an Epanechnikov type kernel is the only admissible kernel. An analysis of kernel density estimates leads to two new methods of bias reduction. We also discuss a general method of improving kernel density estimates in the sense of having smaller mean squared error.
M. Samiuddin, G. M. El-Sayyad
openaire   +1 more source

Data Structures in Kernel Density Estimation

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1985
We analyze and compare several data structures and algorithms for evaluating the kernel density estimate. Frequent evaluations of this estimate are for example needed for plotting, error estimation, Monte Carlo estimation of probabilities and functionals, and pattern classification. An experimental comparison is included.
Devroye, Luc, Machell, Fred
openaire   +2 more sources