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Kernel density estimation: the general case
Statistics & Probability Letters, 2001zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Campos, V. S. M., Dorea, C. C. Y.
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2017
This chapter describes the kernel density estimation technique that can be considered a smoothed version of the Parzen windows presented in the Chapter 2. First, the most popular kernel types are presented together with a number of basic definitions both for uni- and multivariate cases and then a review of performance criteria is provided, starting ...
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This chapter describes the kernel density estimation technique that can be considered a smoothed version of the Parzen windows presented in the Chapter 2. First, the most popular kernel types are presented together with a number of basic definitions both for uni- and multivariate cases and then a review of performance criteria is provided, starting ...
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1991
The idea of kernel estimators was introduced by Rosenblatt (1956). In Chapter 1 needles were used in the observations as a very noisy method to approximate density.
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The idea of kernel estimators was introduced by Rosenblatt (1956). In Chapter 1 needles were used in the observations as a very noisy method to approximate density.
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Density estimation by entropy maximization with kernels
2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015The estimation of a probability density function is one of the most fundamental problems in statistics. The goal is achieving a desirable balance between flexibility while maintaining as simple a form as possible to allow for generalization, and efficient implementation.
Gengshen Fu +2 more
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TAKDE: Temporal Adaptive Kernel Density Estimator for Real-Time Dynamic Density Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023Yinsong Wang +2 more
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Fast feature selection for interval-valued data through kernel density estimation entropy
International Journal of Machine Learning and Cybernetics, 2020Jianhua Dai, Jiaolong Chen
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Anomaly Detection Using Local Kernel Density Estimation and Context-Based Regression
IEEE Transactions on Knowledge and Data Engineering, 2020Weiming Hu, Bing Li, Ou Wu
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Tessellation-based Kernel Density Estimation
2021 4th International Conference on Algorithms, Computing and Artificial Intelligence, 2021Vladislav Belov, Radek Marik
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A tutorial on kernel density estimation and recent advances
Biostatistics and Epidemiology, 2017Yen-Chi Chen
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