Effective Data Density Estimation in Ring-Based P2P Networks

2012 ◽  
Author(s):  
Minqi Zhou ◽  
Heng Tao Shen ◽  
Xiaofang Zhou ◽  
Weining Qian ◽  
Aoying Zhou
Keyword(s):  
Density Estimation ◽  
P2p Networks ◽  
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

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

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 Density Estimation by Mixture Models with Smoothing Priors

1998 ◽  
Vol 10 (8) ◽  
pp. 2115-2135 ◽  
Author(s):  
Akio Utsugi
Keyword(s):  
Density Estimation ◽  
Estimation Algorithm ◽  
Gaussian Mixture ◽  
Self Organizing Maps ◽  
High Data ◽  
Common Value ◽  
Component Selection ◽  
Data Density ◽  
Som Model ◽  
Selection Probabilities

In the statistical approach for self-organizing maps (SOMs), learning is regarded as an estimation algorithm for a gaussian mixture model with a gaussian smoothing prior on the centroid parameters. The values of the hyperparameters and the topological structure are selected on the basis of a statistical principle. However, since the component selection probabilities are fixed to a common value, the centroids concentrate on areas with high data density. This deforms a coordinate system on an extracted manifold and makes smoothness evaluation for the manifold inaccurate. In this article, we study an extended SOM model whose component selection probabilities are variable. To stabilize the estimation, a smoothing prior on the component selection probabilities is introduced. An estimation algorithm for the parameters and the hyperparameters based on empirical Bayesian inference is obtained. The performance of density estimation by the new model and the SOM model is compared via simulation experiments.

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