Variable Kernel Estimates of Multivariate Densities

On Estimation of a Probability Density Function and Mode

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An Algorithm for Finding Best Matches in Logarithmic Expected Time

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A Nonparametric Estimate of a Multivariate Density Function

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Nonparametric probability density estimation

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Characterization of cyclostationary random signal processes

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Patterns in pattern recognition: 1968-1974

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Nonparametric Probability Density Estimation: I. A Summary of Available Methods

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Frontiers of Pattern Recognition

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Nonparametric estimates of probability densities

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A HIERARCHY OF PROBABILITY DENSITY FUNCTION ESTIMATES

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Urban Versus Rural

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The welfare cost of means‐testing: pensioner participation in income support

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A simulative comparison of linear, quadratic and kernel discrimination

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M3W: Multistep Three-Way Clustering

Du, M., Zhao, J., Sun, J., & Dong, Y. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3208418 · Cited by 47

Faithful representations with topographic maps

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Joint Entropy Maximization in Kernel-Based Topographic Maps

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Brainstorm: Occupational choice, bipolar illness and creativity

Tremblay, C. H., Grosskopf, S., & Yang, K. (2010) · Economics & Human Biology · doi:10.1016/j.ehb.2010.01.001 · Cited by 46

Local data-driven bandwidth choice for density estimation

Mielniczuk, J., Sarda, P., & Vieu, P. (1989) · Journal of Statistical Planning and Inference · doi:10.1016/0378-3758(89)90039-6 · Cited by 44

Fast Kernel Smoothing of Point Patterns on a Large Network using Two‐dimensional Convolution

Rakshit, S., Davies, T. M., Moradi, M., McSwiggan, G., Nair, G. M., Mateu, J., & Baddeley, A. J. (2019) · International Statistical Review · doi:10.1111/insr.12327 · Cited by 44

An Application of Imputation to an Estimation Problem in Grouped Lifetime Analysis

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Spatial organization of the North Island Brown Kiwi Apteryx australis mantelli: sex, pairing status and territoriality

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Risk analysis for flood control operation of seasonal flood-limited water level incorporating inflow forecasting error

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Comparison of density estimation methods for astronomical datasets

Ferdosi, B. J., Buddelmeijer, H., Trager, S. C., Wilkinson, M. H. F., & Roerdink, J. B. T. M. (2011) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201116878 · Cited by 41

Analysing Convergence through the Distribution Dynamics Approach: Why and How?Preprint

Magrini, S. (2007) · SSRN Electronic Journal · doi:10.2139/ssrn.1011946 · Cited by 41

Riskiness-minimizing spot-futures hedge ratio

Chen, Y., Ho, K., & Tzeng, L. Y. (2013) · Journal of Banking & Finance · doi:10.1016/j.jbankfin.2013.11.038 · Cited by 40

Variable location and scale kernel density estimation

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Probabilistic failure analysis, performance assessment, and sensitivity analysis of corroded reinforced concrete structures

Guo, H., Dong, Y., Bastidas‐Arteaga, E., & Gu, X.-L. (2021) · Engineering Failure Analysis · doi:10.1016/j.engfailanal.2021.105328 · Cited by 39

Nonparametric Probabilistic Unbalanced Power Flow With Adaptive Kernel Density Estimator

Nosratabadi, H., Mohammadi, M., & Kargarian, A. (2018) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2018.2823058 · Cited by 38

Information-Theoretic Analysis of Serial Dependence and Cointegration

Aparicio, F., & Escribano, Á. (1998) · Studies in Nonlinear Dynamics and Econometrics · doi:10.2202/1558-3708.1044 · Cited by 38

On Global Properties of Variable Bandwidth Density Estimators

Hall, P. M. (1992) · The Annals of Statistics · doi:10.1214/aos/1176348655 · Cited by 38

Rate of strong uniform convergence of k-NN density estimates

Mack, Y. P. (1983) · Journal of Statistical Planning and Inference · doi:10.1016/0378-3758(83)90037-x · Cited by 37

Robust NL-Means Filter With Optimal Pixel-Wise Smoothing Parameter for Statistical Image Denoising

Doré, V., & Cheriet, M. (2009) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2008.2011832 · Cited by 37

Kernel Density Estimation: Theory and Application in Discriminant Analysis

Ledl, T. (2016) · Austrian Journal of Statistics · doi:10.17713/ajs.v33i3.441 · Cited by 37

Blind assessment of localisation microscope image resolution

Rees, E. J., Erdélyi, M., Pinotsi, D., Knight, A. E., Metcalf, D. J., & Kaminski, C. F. (2012) · Optical Nanoscopy · doi:10.1186/2192-2853-1-12 · Cited by 36

Nearly optimal neural network stabilization of bipedal standing using genetic algorithm

Ghorbani, R., Wu, Q., & Wang, G. G. (2006) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2006.09.007 · Cited by 35

Mean-Shift-Based Speckle Filtering of Polarimetric SAR Data

Lang, F., Yang, J., Li, D., Shi, L., & Wei, J. (2013) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2013.2282036 · Cited by 34

Efficiently answering top-k typicality queries on large databases

Ming, H., Pei, J., Fu, A. W.-C., Lin, X., & Leung, H.-F. (2007) · Cited by 34

Optimal smoothing parameters for multivariate fized and adaptive kernel methods

Worton, B. J. (1989) · Journal of Statistical Computation and Simulation · doi:10.1080/00949658908811152 · Cited by 34

Parallel distributed kernel estimation

Racine, J. S. (2002) · Computational Statistics & Data Analysis · doi:10.1016/s0167-9473(01)00109-8 · Cited by 33

Finding and visualizing relevant subspaces for clustering high-dimensional astronomical data using connected morphological operators

Ferdosi, B. J., Buddelmeijer, H., Trager, S. C., Wilkinson, M. H. F., & Roerdink, J. B. T. M. (2010) · IEEE Conference on Visual Analytics Science and Technology (VAST) · doi:10.1109/vast.2010.5652450 · Cited by 32

Lagrangian Modeling of Reactive Transport in Heterogeneous Porous Media With an Automatic Locally Adaptive Particle Support Volume

Sole‐Mari, G., & Fernàndez‐Garcia, D. (2018) · Water Resources Research · doi:10.1029/2018wr023033 · Cited by 33

Clusterdv: a simple density-based clustering method that is robust, general and automatic

Marques, J. C., & Orger, M. B. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty932 · Cited by 31

Nonparametric variable kernel estimation with historical floods and paleoflood information

Guo, S. (1991) · Water Resources Research · doi:10.1029/90wr01972 · Cited by 31

A variable bandwidth selector in multivariate kernel density estimation

Wu, T.-J., Chen, C., & Chen, H.-Y. (2006) · Statistics & Probability Letters · doi:10.1016/j.spl.2006.08.013 · Cited by 30

Bivariate B-splines for tensor logspline density estimation

Koo, J. (1996) · Computational Statistics & Data Analysis · doi:10.1016/0167-9473(95)00003-8 · Cited by 30

Bayesian adaptive bandwidth kernel density estimation of irregular multivariate distributions

Hu, S., Poskitt, D. S., & Zhang, X. (2011) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2011.09.022 · Cited by 29

Reducing bias in curve estimation by use of weights

Hall, P. M., & Turlach, B. A. (1999) · Computational Statistics & Data Analysis · doi:10.1016/s0167-9473(98)00081-4 · Cited by 29

DHeat: A Density Heat-Based Algorithm for Clustering With Effective Radius

Chen, Y., Tang, S., Pei, S., Wang, C., Du, J., & Xiong, N. N. (2017) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2017.2745493 · Cited by 28

Fuzzy approach to analysis of flood risk based on variable fuzzy sets and improved information diffusion methods

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On the variable bandwidth kernel estimation of conditional U -statistics at optimal rates in sup-norm

Bouzebda, S., & Taachouche, N. (2023) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2023.129000 · Cited by 29

CORRECTING FOR KURTOSIS IN DENSITY ESTIMATION

Ruppert, D., & WAND, M. P. (1992) · Australian Journal of Statistics · doi:10.1111/j.1467-842x.1992.tb01039.x · Cited by 27

Binary Vision: The Mass Distribution of Merging Binary Black Holes via Iterative Density Estimation

Sadiq, J., Dent, T., & Gieles, M. (2023) · The Astrophysical Journal · doi:10.3847/1538-4357/ad0ce6 · Cited by 26

A hybrid bandwidth selection methodology for kernel density estimation

Jiang, M., & Provost, S. B. (2012) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2012.721366 · Cited by 26

A Feature-Based Approach to Big Data Analysis of Medical Images

Toews, M., Wachinger, C., Estépar, R. S. J., & Wells, W. M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19992-4_26 · Cited by 25

Kernel regression in mixed feature spaces for spatio-temporal saliency detection

Li, Y., Tan, Y., Yu, J.-G., Qi, S., & Tian, J. (2015) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2015.01.011 · Cited by 25

Classifiers Based on Bayes Decision Theory

Theodoridis, S., & Koutroumbas, K. (2009) · Elsevier eBooks · doi:10.1016/b978-1-59749-272-0.50004-9 · Cited by 25

Filtered kernel density estimation

Marchette, D. J. (2009) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.17 · Cited by 25

On the Estimation of a Convex Set From Noisy Data on Its Support Function

Fisher, N. I., Hall, P. M., Turlach, B. A., & Watson, G. S. (1997) · Journal of the American Statistical Association · doi:10.1080/01621459.1997.10473605 · Cited by 25

TWStream: Three-Way Stream Clustering

Sun, J., Du, M., Lew, Z., & Dong, Y. (2024) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2024.3369716 · Cited by 24

Adaptive Kernel Auxiliary Particle Filter Method for Degradation State Estimation

Lin, Y., & Jiao, X.-L. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.107562 · Cited by 24

12 Nonparametric function estimation

Gasser, T., Engel, J., & Seifert, B. (1993) · Handbook of statistics · doi:10.1016/s0169-7161(05)80136-1 · Cited by 24

A Note on Data-Adaptive Kernel Estimation of the Hazard and Density Function in the Random Censorship Situation

Schafer, H. (1985) · The Annals of Statistics · doi:10.1214/aos/1176349560 · Cited by 24

A framework for annotating OpenStreetMap objects using geo-tagged tweets

Chen, X., Vo, H., Wang, Y., & Wang, F. (2018) · GeoInformatica · doi:10.1007/s10707-018-0323-8 · Cited by 23

Multivariate kernel density estimation with a parametric support

Jarnicka, J. (2009) · Opuscula Mathematica · doi:10.7494/opmath.2009.29.1.41 · Cited by 23

Local Convergence of Empirical Measures in the Random Censorship Situation with Application to Density and Rate Estimators

Schafer, H. (1986) · The Annals of Statistics · doi:10.1214/aos/1176350063 · Cited by 23

Normal transformation for correlated random variables based on L-moments and its application in reliability engineering

Tong, M.-N., Zhao, Y., & Lu, Z. (2020) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2020.107334 · Cited by 22

A three-way clustering method based on improved density peaks algorithm and boundary detection graph

Sun, C., Du, M., Sun, J., Li, K., & Dong, Y. (2022) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2022.12.002 · Cited by 21

Adaptive multichannel filters for colour image processing

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Predicting mobility events on personal devices

Peddemors, A., Eertink, H., & Niemegeers, I. G. M. M. (2009) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2009.11.001 · Cited by 20

The evaluation of evidence relating to traces of cocaine on banknotes

Wilson, A. L., Aitken, C. G. G., Sleeman, R., & Carter, J. F. (2013) · Forensic Science International · doi:10.1016/j.forsciint.2013.11.011 · Cited by 20

Variable Kernel Estimates: on the Impossibility of Tuning the Parameters

Devroye, L., & Lugosi, G. (2000) · Birkhäuser Boston eBooks · doi:10.1007/978-1-4612-1358-1_27 · Cited by 20

Zero‐Bias Locally Adaptive Density Estimators

Sain, S. R., & Scott, D. W. (2002) · Scandinavian Journal of Statistics · doi:10.1111/1467-9469.00300 · Cited by 20

Local Linear M‐estimation in non‐parametric spatial regression

Lin, Z., Li, D., & Gao, J. (2009) · Journal of Time Series Analysis · doi:10.1111/j.1467-9892.2009.00612.x · Cited by 19

Obtaining meaningful results from Fourier deconvolution of reaction time data.

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Kernel methods for the estimation of discrete distributions

Aitken, C. G. G. (1983) · Journal of Statistical Computation and Simulation · doi:10.1080/00949658308810621 · Cited by 19

Bandwidth Selection for Weighted Kernel Density EstimationPreprint

Wang, B., & Wang, X. (2007) · arXiv (Cornell University) · doi:10.48550/arxiv.0709.1616 · Cited by 19

A review of efficient techniques and applications of kernel density in particle filtering framework

Kham, S. L., Chamnongthai, K., & Aunsri, N. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103169 · Cited by 19

Statistical Process Monitoring of Artificial Neural Networks

Malinovskaya, A., Mozharovskyi, P., & Otto, P. (2023) · Technometrics · doi:10.1080/00401706.2023.2239886 · Cited by 21

The automatic model selection and variable kernel width for RBF neural networks

Zhou, P., Li, D., Wu, H. Q., & Feng, C. (2011) · Neurocomputing · doi:10.1016/j.neucom.2011.07.011 · Cited by 18

Simulated minimum Hellinger distance estimation of stochastic volatility models

Takada, T. (2008) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2008.06.017 · Cited by 18

An assessment of finite sample performance of adaptive methods in density estimation

Farmen, M. W., & Marron, J. S. (1999) · Computational Statistics & Data Analysis · doi:10.1016/s0167-9473(98)00070-x · Cited by 18

A general kernel functional estimator with general bandwidth—strong consistency and applications

Weißbach, R. (2006) · Journal of nonparametric statistics · doi:10.1080/10485250500304849 · Cited by 18

Multi-scenario data-driven robust optimisation for industrial steam power systems under uncertainty

Han, Y., Zheng, J., Luo, X., Qian, Y., & Yang, S. (2022) · Energy · doi:10.1016/j.energy.2022.126032 · Cited by 17

A diffusion-based kernel density estimator (diffKDE, version 1) with optimal bandwidth approximation for the analysis of data in geoscience and ecological research

Pelz, M.-T., Schartau, M., Somes, C. J., Lampe, V., & Slawig, T. (2023) · Geoscientific model development · doi:10.5194/gmd-16-6609-2023 · Cited by 21

An Adaptive Covariance Parameterization Technique for the Ensemble Gaussian Mixture Filter

Popov, A. A., & Zanetti, R. (2024) · SIAM Journal on Scientific Computing · doi:10.1137/22m1544312 · Cited by 17

Multivariate functional-coefficient regression models for nonlinear vector time series data

Jiang, J. (2014) · Biometrika · doi:10.1093/biomet/asu011 · Cited by 17

Rotated General Regression Neural Network

Gholamrezaei, M., & Ghorbanian, K. (2007) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2007.4371258 · Cited by 18

Top-k typicality queries and efficient query answering methods on large databases

Ming, H., Pei, J., Fu, A. W.-C., Lin, X., & Leung, H.-F. (2009) · The VLDB Journal · doi:10.1007/s00778-008-0128-8 · Cited by 17

Classifiers Based on Bayes Decision Theory

Theodoridis, S., Pikrakis, A., Koutroumbas, K., & Cavouras, D. (2010) · Elsevier eBooks · doi:10.1016/b978-0-12-374486-9.00001-4 · Cited by 17

On Potts Model Clustering, KernelK-Means and Density Estimation

Murua, A., Stanberry, L. I., & Stuetzle, W. (2008) · Journal of Computational and Graphical Statistics · doi:10.1198/106186008x318855 · Cited by 17

Uniform consistency of automatic and location-adaptive delta-sequence estimators

Nolan, D., & Marron, J. S. (1989) · Probability Theory and Related Fields · doi:10.1007/bf00318909 · Cited by 17

Multi-GPU-Parallel and Tile-Based Kernel Density Estimation for Large-Scale Spatial Point Pattern Analysis

Zhang, G., & Xu, J. (2023) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi12020031 · Cited by 16

Efficient Online Stream Clustering Based on Fast Peeling of Boundary Micro-Cluster

Sun, J., Du, M., Sun, C., & Dong, Y. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3382033 · Cited by 16

Remembering Leo Breiman

Cutler, A. (2010) · The Annals of Applied Statistics · doi:10.1214/10-aoas427 · Cited by 15

A Review on Smoothing Methods for the Estimation of the Hazard Rate Based on Kernel Functions

Gefeller, O., & Michels, P. (1992) · Physica-Verlag eBooks · doi:10.1007/978-3-662-26811-7_63 · Cited by 16

The strong uniform convergence of multivariate variable kernel estimates

Devroye, L., & Penrod, C. S. (1986) · Canadian Journal of Statistics · doi:10.2307/3314798 · Cited by 16

Practical non-parametric density estimation on a transformation group for vision

Miller, E. G., & Chefd’hotel, C. (2003) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2003.1211460 · Cited by 16

Learning to (Learn at Test Time): RNNs with Expressive Hidden States

Sun, Y., Li, X., Dalal, K., Xu, J., Vikram, A., Zhang, G., Dubois, Y., Chen, X., Wang, X., Koyejo, S., et al. (2024) · arXiv · doi:10.48550/arxiv.2407.04620 · Cited by 18

Measuring the elemental abundance and isotopic signature of solar wind xenon collected by the Genesis mission

Crowther, S. A., & Gilmour, J. D. (2011) · Journal of Analytical Atomic Spectrometry · doi:10.1039/c1ja10163c · Cited by 15

Review: Methods of firing rate estimation

Tomar, R. (2019) · Biosystems · doi:10.1016/j.biosystems.2019.103980 · Cited by 15

Local statistics allow quantification of cell-to-cell variability from high-throughput microscope images

Handfield, L., Strome, B., Chong, Y. T., & Moses, A. M. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu759 · Cited by 13

Graph attribute embedding via Riemannian submersion learning

Zhao, H., Robles‐Kelly, A., Zhou, J. W., Lu, J., & Yang, J.-Y. (2011) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2010.12.005 · Cited by 15

FROG: A Fast and Reliable Crowdsourcing Framework

Cheng, P., Lian, X., Jian, X., & Chen, L. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2849394 · Cited by 15

Massive spheroids can form in single minor mergers

Jackson, R. A., Martin, G., Kaviraj, S., Laigle, C., Devriendt, J., Dubois, Y., & Pichon, C. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz2440 · Cited by 15

ATS Methods: Nonparametric Regression for Non-Gaussian Data

Cleveland, W. S., Mallows, C. L., & McRae, J. E. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10476347 · Cited by 15

A Moving Ellipsoid Method for Nonparametric Regression and Its Application to Logit Diagnostics with Scanner Data

Abe, M. (1991) · Journal of Marketing Research · doi:10.1177/002224379102800308 · Cited by 15

A Nonparametric Analysis of the Shape Dynamics of the Us Personal Income Distribution: 1962-2000Preprint

Zhu, F. (2007) · SSRN Electronic Journal · doi:10.2139/ssrn.845708 · Cited by 15

Identifying the progenitors of present-day early-type galaxies in observational surveys: correcting ‘progenitor bias’ using the Horizon-AGN simulation

Martin, G., Kaviraj, S., Devriendt, J., Dubois, Y., Pichon, C., & Laigle, C. (2017) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stx3057 · Cited by 14

Adaptive bandwidth kernel density estimation for next-generation sequencing data

Ramachandran, P., & Perkins, T. J. (2013) · BMC Proceedings · doi:10.1186/1753-6561-7-s7-s7 · Cited by 15

INCOME DISPARITIES IN THE ENLARGED EU: SOCIO‐ECONOMIC, SPECIALISATION AND GEOGRAPHICAL CLUSTERS

Chapman, S. A., & Meliciani, V. (2011) · Tijdschrift voor Economische en Sociale Geografie · doi:10.1111/j.1467-9663.2011.00683.x · Cited by 14

A NOTE ON THE USEFULNESS OF SUIPERKERNELS IN DENSITY ESTIMATION' BY Luc DEVROYE

(2008)

A Note on the Usefulness of Superkernels in Density Estimation

Devroye, L. (1992) · The Annals of Statistics · doi:10.1214/aos/1176348901 · Cited by 81

The strong uniform convergence of multivariate variable kernel estimates

Devroye, L., & Penrod, C. S. (1986) · Canadian Journal of Statistics · doi:10.2307/3314798 · Cited by 16

A comparative study of some kernel-based nonparametric density estimators

BOWMAN, A. W. (1985) · Journal of Statistical Computation and Simulation · doi:10.1080/00949658508810822 · Cited by 120

A nonparametric data based univariate density function estimate

Kappenman, R. F. (1987) · Computational Statistics & Data Analysis · doi:10.1016/0167-9473(87)90002-8 · Cited by 6