scholarly journals On bandwidth choice for spatial data density estimation

2020 ◽  
Vol 82 (3) ◽  
pp. 817-840
Author(s):  
Zhenyu Jiang ◽  
Nengxiang Ling ◽  
Zudi Lu ◽  
Dag Tj⊘stheim ◽  
Qiang Zhang
Keyword(s):  
Density Estimation ◽  
Spatial Data ◽  
Data Density ◽  
Data Density Estimation

Multi-dimensional data density estimation in P2P networks

2009 ◽  
Vol 26 (2-3) ◽  
pp. 261-289 ◽  
Author(s):  
Minqi Zhou ◽  
Weining Qian ◽  
Xueqing Gong ◽  
Aoying Zhou
Keyword(s):  
Density Estimation ◽  
P2p Networks ◽  
Data Density ◽  
Data Density Estimation

Distribution-free data density estimation in large-scale networks

2018 ◽  
Vol 12 (6) ◽  
pp. 1220-1240
Author(s):  
Minqi Zhou ◽  
Rong Zhang ◽  
Weining Qian ◽  
Aoying Zhou
Keyword(s):  
Density Estimation ◽  
Large Scale ◽  
Distribution Free ◽  
Free Data ◽  
Data Density ◽  
Large Scale Networks ◽  
Data Density Estimation

On the Use of a Modified Intersection of Confidence Intervals (MICIH) Kernel Density Estimation Approach

2021 ◽  
Vol 8 (4) ◽  
pp. 309-332
Author(s):  
Efosa Michael Ogbeide ◽  
Joseph Erunmwosa Osemwenkhae
Keyword(s):  
Confidence Intervals ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Mean Squared Error ◽  
Kernel Density ◽  
Window Size ◽  
National Bureau ◽  
Squared Error ◽  
Data Density ◽  
Adaptive Kernel

Density estimation is an important aspect of statistics. Statistical inference often requires the knowledge of observed data density. A common method of density estimation is the kernel density estimation (KDE). It is a nonparametric estimation approach which requires a kernel function and a window size (smoothing parameter H). It aids density estimation and pattern recognition. So, this work focuses on the use of a modified intersection of confidence intervals (MICIH) approach in estimating density. The Nigerian crime rate data reported to the Police as reported by the National Bureau of Statistics was used to demonstrate this new approach. This approach in the multivariate kernel density estimation is based on the data. The main way to improve density estimation is to obtain a reduced mean squared error (MSE), the errors for this approach was evaluated. Some improvements were seen. The aim is to achieve adaptive kernel density estimation. This was achieved under a sufficiently smoothing technique. This adaptive approach was based on the bandwidths selection. The quality of the estimates obtained of the MICIH approach when applied, showed some improvements over the existing methods. The MICIH approach has reduced mean squared error and relative faster rate of convergence compared to some other approaches. The approach of MICIH has reduced points of discontinuities in the graphical densities the datasets. This will help to correct points of discontinuities and display adaptive density. Keywords: approach, bandwidth, estimate, error, kernel density

scholarly journals Nonparametric recursive density estimation for spatial data

2016 ◽  
Vol 354 (2) ◽  
pp. 205-210 ◽  
Author(s):  
Aboubacar Amiri ◽  
Sophie Dabo-Niang ◽  
Mohamed Yahaya
Keyword(s):  
Density Estimation ◽  
Spatial Data

Effects Of Spatial Data Density, Sensor Noise And Position Errors On Uxo And Clutter Target Parameters From Inversions Of Alltem Data

2008 ◽  
Author(s):  
David L. Wright ◽  
Theodore H. Asch ◽  
Craig W. Moulton ◽  
Trevor P. Irons ◽  
Misac N. Nabighian
Keyword(s):  
Spatial Data ◽  
Sensor Noise ◽  
Position Errors ◽  
Data Density

Effects of Spatial Data Density, Sensor Noise and Position Errors on UXO and Clutter Target Parameters from Inversions of ALLTEM Data

2008 ◽  
Author(s):  
David L. Wright ◽  
Theodore H. Asch ◽  
Craig W. Moulton ◽  
Trevor P. Irons ◽  
Misac N. Nabighian
Keyword(s):  
Spatial Data ◽  
Sensor Noise ◽  
Position Errors ◽  
Data Density
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