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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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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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Kernel Density Estimation on a Linear Network
Scandinavian Journal of Statistics, 2016AbstractThis paper develops a statistically principled approach to kernel density estimation on a network of lines, such as a road network. Existing heuristic techniques are reviewed, and their weaknesses are identified. The correct analogue of the Gaussian kernel is the ‘heat kernel’, the occupation density of Brownian motion on the network.
Mcswiggan, G. +2 more
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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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Kernel Density Estimation for Compositional Data
Applied Statistics, 1985Summary: Although rich parametric families of distributions over the simplex now exist for describing patterns of variability of compositional data, there remain problems where such descriptions fail. For such cases this paper suggests two main kernel methods of density estimation and compares their performance on real and simulated data sets.
Aitchison, J., Lauder, I. J.
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TAKDE: Temporal Adaptive Kernel Density Estimator for Real-Time Dynamic Density Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023Yu Ding +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, 2020Jiaolong Chen, Jianhua Dai
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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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Anomaly Detection Using Local Kernel Density Estimation and Context-Based Regression
IEEE Transactions on Knowledge and Data Engineering, 2020Junping Du, Weiming Hu, Bing Li
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