A Non-Parametric Method to Determine Basic Probability Assignment Based on Kernel Density Estimation

IEEE Access ◽

10.1109/access.2018.2883513 ◽

2018 ◽

Vol 6 ◽

pp. 73509-73519 ◽

Cited By ~ 4

Author(s):

Bowen Qin ◽

Fuyuan Xiao

Keyword(s):

Density Estimation ◽

Kernel Density Estimation ◽

Parametric Method ◽

Kernel Density ◽

Probability Assignment ◽

Basic Probability Assignment ◽

Basic Probability ◽

Non Parametric

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A non-parametric method to determine basic probability assignment for classification problems

Applied Intelligence ◽

10.1007/s10489-014-0546-9 ◽

2014 ◽

Vol 41 (3) ◽

pp. 681-693 ◽

Cited By ~ 23

Author(s):

Peida Xu ◽

Xiaoyan Su ◽

Sankaran Mahadevan ◽

Chenzhao Li ◽

Yong Deng

Keyword(s):

Parametric Method ◽

Classification Problems ◽

Probability Assignment ◽

Basic Probability Assignment ◽

Basic Probability ◽

Non Parametric

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Short-term wind speed forecasting based on non-parametric kernel density estimation

IEEE PES Innovative Smart Grid Technologies ◽

10.1109/isgt-asia.2012.6303377 ◽

2012 ◽

Cited By ~ 1

Author(s):

Jiegui Zhou ◽

Jia Hongjie ◽

Zhen Tian ◽

Linlin Hu

Keyword(s):

Wind Speed ◽

Density Estimation ◽

Kernel Density Estimation ◽

Kernel Density ◽

Short Term ◽

Wind Speed Forecasting ◽

Non Parametric

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Wind power prediction errors model and algorithm based on non-parametric kernel density estimation

2015 5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT) ◽

10.1109/drpt.2015.7432551 ◽

2015 ◽

Cited By ~ 1

Author(s):

Guodong Liao ◽

Jie Ming ◽

Boyuan Wei ◽

Hongji Xiang ◽

Nan Jiang Peng Ai ◽

...

Keyword(s):

Wind Power ◽

Density Estimation ◽

Kernel Density Estimation ◽

Kernel Density ◽

Prediction Errors ◽

Power Prediction ◽

Wind Power Prediction ◽

Non Parametric

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A non-parametric approach for independent component analysis using kernel density estimation

Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. ◽

10.1109/cvpr.2004.1315228 ◽

2004 ◽

Cited By ~ 5

Author(s):

K. Sengupta ◽

P. Burman ◽

R. Sharma

Keyword(s):

Independent Component Analysis ◽

Density Estimation ◽

Kernel Density Estimation ◽

Kernel Density ◽

Component Analysis ◽

Independent Component ◽

Parametric Approach ◽

Non Parametric

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Non-parametric kernel density estimation of species sensitivity distributions in developing water quality criteria of metals

Environmental Science and Pollution Research ◽

10.1007/s11356-015-4602-8 ◽

2015 ◽

Vol 22 (18) ◽

pp. 13980-13989 ◽

Cited By ~ 7

Author(s):

Ying Wang ◽

Fengchang Wu ◽

John P. Giesy ◽

Chenglian Feng ◽

Yuedan Liu ◽

...

Keyword(s):

Water Quality ◽

Density Estimation ◽

Kernel Density Estimation ◽

Kernel Density ◽

Quality Criteria ◽

Water Quality Criteria ◽

Species Sensitivity ◽

Non Parametric ◽

Species Sensitivity Distributions

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Automatic Robust Background Modeling Using Multivariate Non-parametric Kernel Density Estimation for Visual Surveillance

Advances in Visual Computing - Lecture Notes in Computer Science ◽

10.1007/11595755_44 ◽

2005 ◽

pp. 363-370 ◽

Cited By ~ 4

Author(s):

Alireza Tavakkoli ◽

Mircea Nicolescu ◽

George Bebis

Keyword(s):

Density Estimation ◽

Kernel Density Estimation ◽

Kernel Density ◽

Background Modeling ◽

Visual Surveillance ◽

Non Parametric

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Comparative study of species sensitivity distributions based on non-parametric kernel density estimation for some transition metals

Environmental Pollution ◽

10.1016/j.envpol.2016.11.084 ◽

2017 ◽

Vol 221 ◽

pp. 343-350 ◽

Cited By ~ 6

Author(s):

Ying Wang ◽

Chenglian Feng ◽

Yuedan Liu ◽

Yujie Zhao ◽

Huixian Li ◽

...

Keyword(s):

Transition Metals ◽

Comparative Study ◽

Density Estimation ◽

Kernel Density Estimation ◽

Kernel Density ◽

Species Sensitivity ◽

Non Parametric ◽

Species Sensitivity Distributions

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Wind Power Prediction Based on Kalman Filter and Non-parametric Kernel Density Estimation

10.1109/icpsasia52756.2021.9621598 ◽

2021 ◽

Author(s):

Daoqing Li ◽

Adil Hussain ◽

Xiaodong Yu ◽

Shulin Liu ◽

Xuanzhou Yu ◽

...

Keyword(s):

Kalman Filter ◽

Wind Power ◽

Density Estimation ◽

Kernel Density Estimation ◽

Kernel Density ◽

Power Prediction ◽

Wind Power Prediction ◽

Non Parametric

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Non-Parametric Probabilistic Demand Forecasting in Distribution Grids; Kernel Density Estimation and Mixture Density Networks

2020 52nd North American Power Symposium (NAPS) ◽

10.1109/naps50074.2021.9449809 ◽

2021 ◽

Author(s):

R.D. Patel ◽

H. Nazaripouya ◽

H. Akhavan-Hejazi

Keyword(s):

Density Estimation ◽

Kernel Density Estimation ◽

Demand Forecasting ◽

Kernel Density ◽

Mixture Density ◽

Distribution Grids ◽

Non Parametric

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Kernel density estimation in regional convergence studies

Wiadomości Statystyczne. The Polish Statistician ◽

10.5604/01.3001.0014.1106 ◽

2016 ◽

Vol 61 (10) ◽

pp. 7-21

Author(s):

Piotr Wójcik

Keyword(s):

Density Estimation ◽

Kernel Density Estimation ◽

Estimation Method ◽

Kernel Density ◽

Regional Convergence ◽

Lower Secondary School ◽

Convergence Hypothesis ◽

Educational Achievements ◽

Science Part ◽

Non Parametric

The aim of the article is to present a non-parametric kernel density estimation method as a tool used for empirical verification of the regional convergence hypothesis, including convergence of clubs. It is explained how kernel density estimation complements other methods applied to verify the phenomenon of convergence. The empirical part shows an application of the non-parametric density estimation to the analysis of regional convergence of educational achievements of Polish pupils, measured by the average results of the mathematical and natural science part of the lower-secondary school leaving exams on the level of municipalities in years 2002—2013. The results of the analysis indicate the existence of regional convergence of exam results for Polish municipalities. In case of the analysis for three-yearly periods convergence of clubs was observed — the municipalities with lowest exam results constitute a separate club of convergence.

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