A Monte Carlo and Kernel Density Estimation based virtual sample generation method for small data modeling problem

2020 ◽  
Author(s):  
Qun-Xiong Zhu ◽  
Zhi-Hui Wang ◽  
Yan-Lin He ◽  
Yuan Xu
Keyword(s):  
Monte Carlo ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Data Modeling ◽  
Kernel Density ◽  
Small Data ◽  
Virtual Sample ◽  
Modeling Problem

BIAS CORRECTION AT END POINTS IN KERNEL DENSITY ESTIMATION

2021 ◽  
Vol 10 (12) ◽  
pp. 3515-3531
Author(s):  
H. Bouredji ◽  
A. Sayah
Keyword(s):  
Monte Carlo ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Bias Correction ◽  
Kernel Density ◽  
Real Data ◽  
Boundary Region ◽  
Finite Sample ◽  
New Approach ◽  
The Right

In this paper, we propose a new approach of boundary correction for kernel density estimation with the support $[0,1]$, in particular at the right endpoints and we derive the theoretical properties of this new estimator and show that it asymptotically reduce the order of bias at the boundary region, whereas the order of variance remains unchanged. Our Monte Carlo simulations demonstrate the good finite sample performance of our proposed estimator. Two examples with real data are provided.

scholarly journals Bearings-only multitarget tracking based onRao-Blackwellized particle CPHD filter

2021 ◽  
Vol 14 ◽  
pp. 1129-1136
Author(s):  
Jungen Zhang
Keyword(s):  
Monte Carlo ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Sequential Monte Carlo ◽  
Simulation Experiment ◽  
Kernel Density ◽  
Multitarget Tracking ◽  
Probability Hypothesis Density ◽  
Cphd Filter ◽  
Nonlinear State

Following Mahler’s framework forinformation fusion, this paper develops a implementationof cardinalized probability hypothesis density (CPHD)filter for bearings-only multitarget tracking.Rao-Blackwellized method is introduced in the CPHDfiltering framework for mixed linear/nonlinear state spacemodels. The sequential Monte Carlo (SMC) method is usedto predict and estimate the nonlinear state of targets.Kalman filter (KF) is adopted to estimate the linear stateswith the information embedded in the estimated nonlinearstates. The multitarget state estimates are extracted byutilizing the kernel density estimation (KDE) theory andmean-shift algorithm to enhance tracking performance.Moreover, the computational load of the filter is analyzedby introducing equivalent flop measure. Finally, theperformance of the proposed Rao-Blackwellized particleCPHD filter is evaluated through a challengingbearings-only multitarget tracking simulation experiment.

scholarly journals Author Correction: An extended Weight Kernel Density Estimation model forecasts COVID-19 onset risk and identifies spatiotemporal variations of lockdown effects in China

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Wenzhong Shi ◽  
Chengzhuo Tong ◽  
Anshu Zhang ◽  
Bin Wang ◽  
Zhicheng Shi ◽  
...  
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Spatiotemporal Variations ◽  
Estimation Model

A Correction to this paper has been published: https://doi.org/10.1038/s42003-021-01924-6

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