Cited by 489Statistical Methods and ApplicationsStatistical Methods and InferenceStatistical Distribution Estimation and Applications

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

Variable Kernel Estimates of Multivariate Densities

Breiman, Leo, Meisel, William, Purcell, Edward·1977·Technometrics

DOI 10.1080/00401706.1977.10489521

Open this paper in CoChat — free account

Abstract

A class of density estimates using a superposition of kernels where the kernel parameter can depend on the nearest neighbor distances is studied by the use of simulated data. Their performance using several measures of error is superior to that of the usual Parzen estimators. A tentative solution is given to the problem of calibrating the kernel peakedness when faced with a finite sample set.

Integrity signals

Registry

Crossref · 10.1080/00401706.1977.10489521

Cites

10 of 10 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

On Estimation of a Probability Density Function and Mode
Parzen, E. (1962) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177704472 · Cited by 10,831
An Algorithm for Finding Best Matches in Logarithmic Expected Time
Friedman, J. H., Bentley, J. L., & Finkel, R. A. (1977) · ACM Transactions on Mathematical Software · doi:10.1145/355744.355745 · Cited by 2,802
A Nonparametric Estimate of a Multivariate Density Function
Loftsgaarden, D. O., & Quesenberry, C. P. (1965) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177700079 · Cited by 800
Nonparametric probability density estimation
Tapia, R. A., & Thompson, J. (1978) · Cited by 454
Characterization of cyclostationary random signal processes
Gardner, W. A., & Franks, L. (1975) · IEEE Transactions on Information Theory · doi:10.1109/tit.1975.1055338 · Cited by 424
Patterns in pattern recognition: 1968-1974
Kanal, L. N. (1974) · IEEE Transactions on Information Theory · doi:10.1109/tit.1974.1055306 · Cited by 282
Nonparametric Probability Density Estimation: I. A Summary of Available Methods
Wegman, E. J. (1972) · Technometrics · doi:10.1080/00401706.1972.10488943 · Cited by 237
Frontiers of Pattern Recognition
(1972) · Elsevier eBooks · doi:10.1016/c2013-0-11670-4 · Cited by 237
Nonparametric estimates of probability densities
Wagner, T. J. (1975) · IEEE Transactions on Information Theory · doi:10.1109/tit.1975.1055408 · Cited by 65
A HIERARCHY OF PROBABILITY DENSITY FUNCTION ESTIMATES
Cover, T. M. (1972) · Elsevier eBooks · doi:10.1016/b978-0-12-737140-5.50010-5 · Cited by 20

Show all 10 references

Research works printed but not linked: 2

Breiman L., 1975, J. Amer. Statist. Assoc.
Meisel, W. “The Complete Pattern Recognition Algorithm”. 8th Annual Symposium on the Interface Between Computer Science and Statistics, Health Sciences Computing Facility, UCLA.

Sorted by citations

A general regression neural network
Specht, D. F. (1991) · IEEE Transactions on Neural Networks · doi:10.1109/72.97934 · Cited by 4,505
Kernel Methods for Estimating the Utilization Distribution in Home‐Range Studies
Worton, B. J. (1989) · Ecology · doi:10.2307/1938423 · Cited by 4,185
Wavelet Shrinkage: Asymptopia?
Donoho, D. L., Johnstone, I. M., Kerkyacharian, G., & Picard, D. (1995) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1995.tb02032.x · Cited by 1,752
An Evaluation of the Accuracy of Kernel Density Estimators for Home Range Analysis
Seaman, D. E., & Powell, R. A. (1996) · Ecology · doi:10.2307/2265701 · Cited by 1,672
On the visualisation of detrital age distributions
Vermeesch, P. (2012) · Chemical Geology · doi:10.1016/j.chemgeo.2012.04.021 · Cited by 1,566
A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions
Shapovalov, M. V., & Dunbrack, R. L. (2011) · Structure · doi:10.1016/j.str.2011.03.019 · Cited by 1,000
Quantitative fluorescence in situ hybridization of Bifidobacterium spp. with genus-specific 16S rRNA-targeted probes and its application in fecal samples
Langendijk-Genevaux, P. S., Schut, F., Jansen, G. J., Raangs, G. C., Kamphuis, G. R., Wilkinson, M. H. F., & Welling, G. W. (1995) · Applied and Environmental Microbiology · doi:10.1128/aem.61.8.3069-3075.1995 · Cited by 974
A tutorial on kernel density estimation and recent advances
Chen, Y. (2017) · Biostatistics & Epidemiology · doi:10.1080/24709360.2017.1396742 · Cited by 937
Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest
Oliveira, S., Oehler, F., San-Miguel-Ayanz, J., Camia, A., & Pereira, J. M. C. (2012) · Forest Ecology and Management · doi:10.1016/j.foreco.2012.03.003 · Cited by 755
A review of models of home range for animal movement
Worton, B. J. (1987) · Ecological Modelling · doi:10.1016/0304-3800(87)90101-3 · Cited by 718
Variable Bandwidth and Local Linear Regression Smoothers
B, J. F. A., & Gijbels, I. (1992) · The Annals of Statistics · doi:10.1214/aos/1176348900 · Cited by 636
Motion-based background subtraction using adaptive kernel density estimation
Mittal, A., & Nikos Paragios, A. (2004) · 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.2004.1315179 · Cited by 591
A new adaptive importance sampling scheme for reliability calculations
Au, S., & Beck, J. L. (1999) · Structural Safety · doi:10.1016/s0167-4730(99)00014-4 · Cited by 574
Recent Developments in Nonparametric Density Estimation
Izenman, A. J. (1991) · Journal of the American Statistical Association · doi:10.2307/2289732 · Cited by 464
Monte Carlo Methods of Inference for Implicit Statistical Models
Diggle, P. J., & Gratton, R. J. (1984) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1984.tb01290.x · Cited by 425
Kernel bandwidth optimization in spike rate estimation
Shimazaki, H., & Shinomoto, S. (2009) · Journal of Computational Neuroscience · doi:10.1007/s10827-009-0180-4 · Cited by 371
Image Segmentation Using Active Contours Driven by the Bhattacharyya Gradient Flow
Michailovich, O., Yogesh Rathi, A., & Tannenbaum, A. (2007) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.908073 · Cited by 321
Outlier Detection with Kernel Density Functions
Latecki, L. J., Lazarević, A. M., & Pokrajac, D. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-73499-4_6 · Cited by 302
Transformations in Density Estimation
WAND, M. P., Marron, J. S., & Ruppert, D. (1991) · Journal of the American Statistical Association · doi:10.1080/01621459.1991.10475041 · Cited by 303
Multivariate k-nearest neighbor density estimates
Mack, Y. P., & Rosenblatt, M. (1979) · Journal of Multivariate Analysis · doi:10.1016/0047-259x(79)90065-4 · Cited by 297
Uniform in bandwidth consistency of kernel-type function estimators
Einmahl, U., & Mason, D. M. (2005) · The Annals of Statistics · doi:10.1214/009053605000000129 · Cited by 305
Robust kernel density estimation
Kim, J., & Scott, C. D. (2012) · Journal of Machine Learning Research · doi:10.5555/2503308.2503323 · Cited by 286
Review Papers: Recent Developments in Nonparametric Density Estimation
Izenman, A. J. (1991) · Journal of the American Statistical Association · doi:10.1080/01621459.1991.10475021 · Cited by 281
Vicinal Risk Minimization
Chapelle, O., Weston, J., Bottou, L., & Vapnik, V. N. (2000) · Cited by 244
Statistical profile estimation in database systems
Mannino, M. V., Chu, P.-C., & Sager, T. W. (1988) · ACM Computing Surveys · doi:10.1145/62061.62063 · Cited by 225
Common Structure of Smoothing Techniques in Statistics
Titterington, D. M. (1985) · International Statistical Review · doi:10.2307/1402932 · Cited by 219
Maximum Likelihood Estimation of a Multi-Dimensional Log-Concave Density
Cule, M., Samworth, R. J., & Stewart, M. I. (2010) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2010.00753.x · Cited by 216
Nonparametric Econometrics: A Primer
Racine, J. S. (2008) · Foundations and Trends® in Econometrics · doi:10.1561/0800000009 · Cited by 214
Wavelet Methods for Curve Estimation
Antoniadis, A., Gregoire, G., & McKeague, I. W. (1994) · Journal of the American Statistical Association · doi:10.1080/01621459.1994.10476873 · Cited by 206
Calibrating Confidence Coefficients
Loh, W. (1987) · Journal of the American Statistical Association · doi:10.1080/01621459.1987.10478408 · Cited by 180
Non-linear methods for multivariate statistical calibration and their use in palaeoecology: a comparison of inverse (k-nearest neighbours, partial least squares and weighted averaging partial least squares) and classical approaches
ter Braak, C. J. F. (1995) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(95)00002-e · Cited by 171
Robust Kernel Density EstimationPreprint
Kim, J., & Scott, C. D. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1107.3133 · Cited by 168
Philatelic Mixtures and Multimodal Densities
Izenman, A. J., & Sommer, C. J. (1988) · Journal of the American Statistical Association · doi:10.1080/01621459.1988.10478683 · Cited by 164
Boletín de la Asociación de Geógrafos Españoles
Españoles, A. D. G. (1979) · Cited by 163
Multidimensional Density Estimation
Scott, D. W., & Sain, S. R. (2005) · Handbook of statistics · doi:10.1016/s0169-7161(04)24009-3 · Cited by 161
Variable Bandwidth Kernel Estimators of Regression Curves
Müller, H., & Stadtmüller, U. (1987) · The Annals of Statistics · doi:10.1214/aos/1176350260 · Cited by 157
Local Properties of k -NN Regression Estimates
Mack, Y. P. (1981) · SIAM Journal on Algebraic and Discrete Methods · doi:10.1137/0602035 · Cited by 156
LoMar: A Local Defense Against Poisoning Attack on Federated Learning
Li, X., Qu, Z., Zhao, S., Tang, B., Lu, Z., & Liu, Y. (2021) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2021.3135422 · Cited by 158
Fuzzy basis functions: comparisons with other basis functions
Kim, H. M., & Mendel, J. M. (1995) · IEEE Transactions on Fuzzy Systems · doi:10.1109/91.388171 · Cited by 150
Logistic regression models for human-caused wildfire risk estimation: analysing the effect of the spatial accuracy in fire occurrence data
Vilar, L., Isabel, M. P. M., & Vega, J. M. (2011) · European Journal of Forest Research · doi:10.1007/s10342-011-0488-2 · Cited by 142
On the effect of estimating the error density in nonparametric deconvolution
Neumann, M. H., & Hössjer, O. (1997) · Journal of nonparametric statistics · doi:10.1080/10485259708832708 · Cited by 142
Mapping lightning/human-caused wildfires occurrence under ignition point location uncertainty
Amatulli, G., Pérez, F., & de la Riva, J. (2006) · Ecological Modelling · doi:10.1016/j.ecolmodel.2006.08.001 · Cited by 138
The role of mergers in driving morphological transformation over cosmic time
Martin, G., Kaviraj, S., Devriendt, J., Dubois, Y., & Pichon, C. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty1936 · Cited by 136
The Mode Tree: A Tool for Visualization of Nonparametric Density Features
Minnotte, M. C., & Scott, D. W. (1993) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.1993.10474599 · Cited by 135
Modeling human location data with mixtures of kernel densities
Lichman, M., & Smyth, P. (2014) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2623330.2623681 · Cited by 134
On Locally Adaptive Density Estimation
Sain, S. R., & Scott, D. W. (1996) · Journal of the American Statistical Association · doi:10.1080/01621459.1996.10476720 · Cited by 133
The formation and evolution of low-surface-brightness galaxies
Martin, G., Kaviraj, S., Laigle, C., Devriendt, J., Jackson, R. A., Peirani, S., Dubois, Y., Pichon, C., & Slyz, A. D. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz356 · Cited by 146
Recent advances in nonparametric function estimation: Hydrologic applications
Lall, U. (1995) · Reviews of Geophysics · doi:10.1029/95rg00343 · Cited by 129
Multi-dimensional Density Estimation
Scott, D. W., & Sain, S. R. (2004) · Cited by 125
A Comparison of Higher-Order Bias Kernel Density Estimators
Jones, M. C., & Signorini, D. F. (1997) · Journal of the American Statistical Association · doi:10.1080/01621459.1997.10474062 · Cited by 124
Multivariate locally adaptive density estimation
Sain, S. R. (2002) · Computational Statistics & Data Analysis · doi:10.1016/s0167-9473(01)00053-6 · Cited by 121
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
Exponential Inequalities in Nonparametric Estimation
Devroye, L. (1991) · Springer eBooks · doi:10.1007/978-94-011-3222-0_3 · Cited by 115
Defining a novel k-nearest neighbours approach to assess the applicability domain of a QSAR model for reliable predictions
Sahigara, F., Ballabio, D., Todeschini, R., & Consonni, V. (2013) · Journal of Cheminformatics · doi:10.1186/1758-2946-5-27 · Cited by 121
A Novel LMS Algorithm Applied to Adaptive Noise Cancellation
Górriz, J. M., Ramı́rez, J., Cruces, S., Puntonet, C. G., Lang, E. W., & Deniz Erdogmus, B. (2008) · IEEE Signal Processing Letters · doi:10.1109/lsp.2008.2008584 · Cited by 114
Molprobity's ultimate rotamer-library distributions for model validation
Hintze, B. J., Lewis, S. M., Richardson, J. S., & Richardson, D. C. (2016) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25039 · Cited by 121
New insights into Approximate Bayesian Computation
Gérard Biau, A., Cérou, F., & Arnaud Guyader, B. (2015) · Annales de l Institut Henri Poincaré Probabilités et Statistiques · doi:10.1214/13-aihp590 · Cited by 111
Non-Parametric Estimation of Econometric Functionals
Ullah, A. (1988) · Canadian Journal of Economics/Revue canadienne d économique · doi:10.2307/135443 · Cited by 111
Color image processing using adaptive multichannel filters
Plataniotis, K. N., Androutsos, D., Vinayagamoorthy, S., & Venetsanopoulos, A. N. (1997) · IEEE Transactions on Image Processing · doi:10.1109/83.597269 · Cited by 110
Consistent Cross-Validated Density Estimation
Chow, Y. S., Geman, S., & Wu, L.-D. (1983) · The Annals of Statistics · doi:10.1214/aos/1176346053 · Cited by 109
Statistical issues in the analysis of disease mapping data
Pascutto, C., Wakefield, J. C., Best, N., Richardson, S. T., Bernardinelli, L., Staines, A. S., & Elliott, P. (2000) · Statistics in Medicine · doi:10.1002/1097-0258(20000915/30)19:17/18 · Cited by 123
On Modeling Heteroskedasticity: The Student's t and Elliptical Linear Regression Models
Spanos, A. (1994) · Econometric Theory · doi:10.1017/s0266466600008422 · Cited by 103
Variable window width kernel estimates of probability densities
Hall, P. M., & Marron, J. S. (1988) · Probability Theory and Related Fields · doi:10.1007/bf00348751 · Cited by 98
VARIABLE KERNEL DENSITY ESTIMATES AND VARIABLE KERNEL DENSITY ESTIMATES
Jones, M. C. (1990) · Australian Journal of Statistics · doi:10.1111/j.1467-842x.1990.tb01031.x · Cited by 96
Random approximations to some measures of accuracy in nonparametric curve estimation
Marron, J. S., & Härdle, W. K. (1986) · Journal of Multivariate Analysis · doi:10.1016/0047-259x(86)90021-7 · Cited by 96
Assessing long‐term fire risk at local scale by means of decision tree technique
Amatulli, G., Rodrigues, M. J., Trombetti, M., & Lovreglio, R. (2006) · Journal of Geophysical Research Atmospheres · doi:10.1029/2005jg000133 · Cited by 89
The beam radiance estimate for volumetric photon mapping
Jarosz, W., Zwicker, M., & Jensen, H. W. (2008) · ACM SIGGRAPH Courses · doi:10.1145/1401132.1401137 · Cited by 87
Pretectal neurons control hunting behaviour
Antinucci, P., Folgueira, M., & Bianco, I. H. (2019) · eLife · doi:10.7554/elife.48114 · Cited by 86
Research on flood risk analysis and evaluation method based on variable fuzzy sets and information diffusion
Li, Q., Zhou, J., Liu, D., & Jiang, X. (2012) · Safety Science · doi:10.1016/j.ssci.2012.01.007 · Cited by 86
Adaptive Smoothing and Density-Based Tests of Multivariate Normality
BOWMAN, A. W., & Foster, P. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10476304 · Cited by 81
Kernel density estimation with adaptive varying window size
Katkovnik, V. Y., & Ilya Shmulevich, C. (2002) · Pattern Recognition Letters · doi:10.1016/s0167-8655(02)00127-7 · Cited by 80
Kernel flood frequency estimators: Bandwidth selection and kernel choice
Lall, U., Moon, Y., & Bosworth, K. W. (1993) · Water Resources Research · doi:10.1029/92wr02466 · Cited by 80
Robust kernel density estimation
Kim, J., & Scott, C. D. (2008) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2008.4518376 · Cited by 76
A Monte Carlo comparison of parametric and nonparametric estimation of flood frequencies
Adamowski, K. (1989) · Journal of Hydrology · doi:10.1016/0022-1694(89)90290-4 · Cited by 74
Nonparametric information fusion for motion estimation
Comaniciu, D. (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.2003.1211338 · Cited by 73
Kernel-Based Equiprobabilistic Topographic Map Formation
Hulle, M. M. V. (1998) · Neural Computation · doi:10.1162/089976698300017179 · Cited by 71
Data-Based Nonparametric Estimation of the Hazard Function with Applications to Model Diagnostics and Exploratory Analysis
Tanner, M. A., & Wong, W. H. (1984) · Journal of the American Statistical Association · doi:10.1080/01621459.1984.10477081 · Cited by 70
Radial basis function and related models: An overview
Acosta, F. M. A. (1995) · Signal Processing · doi:10.1016/0165-1684(95)00041-b · Cited by 69
Weighted samples, kernel density estimators and convergence
Gisbert, F. J. G. (2003) · Empirical Economics · doi:10.1007/s001810200134 · Cited by 69
Semantic Annotation of Mobility Data using Social Media
Wu, F. Q., Li, Z., Lee, W.-C., Wang, H., & Huang, Z. (2015) · International World Wide Web Conference (WWW) · doi:10.1145/2736277.2741675 · Cited by 67
Kernel Density Estimation Methods for a Geostatistical Approach in Seismic Risk Analysis: The Case Study of Potenza Hilltop Town (Southern Italy)
Danese, M., Lazzari, M., & Murgante, B. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-69839-5_31 · Cited by 67
Decentralized Clustering by Finding Loose and Distributed Density Cores
Chen, Y., Tang, S., Zhou, L., Wang, C., Du, J., Wang, T., & Pei, S. (2016) · Information Sciences · doi:10.1016/j.ins.2016.08.009 · Cited by 67
Dark matter-deficient dwarf galaxies form via tidal stripping of dark matter in interactions with massive companions
Jackson, R. A., Kaviraj, S., Martin, G., Devriendt, J., Slyz, A. D., Silk, J. I., Dubois, Y., Yi, S. K., Pichon, C., Volonteri, M., et al. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab093 · Cited by 65
Variable bandwidth and one-step local M-estimator
B, J. F. A., & Jiang, J. (2000) · Science in China Series A Mathematics · doi:10.1007/bf02903849 · Cited by 65
A quad-tree-based fast and adaptive Kernel Density Estimation algorithm for heat-map generation
Yuan, K., Cheng, X., Gui, Z., Li, F., & Wu, H. (2019) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2018.1555831 · Cited by 68
An algorithm for pattern description on the level of relative proximity
van Oeffelen, M. P., & Vos, P. G. (1983) · Pattern Recognition · doi:10.1016/0031-3203(83)90040-7 · Cited by 63
On the Nonconsistency of Maximum Likelihood Nonparametric Density Estimators
Schuster, E. F., & Gregory, G. G. (1981) · Springer eBooks · doi:10.1007/978-1-4613-9464-8_43 · Cited by 62
Visualization of spatial patterns and temporal trends for aerial surveillance of illegal oil discharges in western Canadian marine waters
Serra-Sogas, N., O’Hara, P. D., Canessa, R. R., Keller, P. C., & Pelot, R. P. (2008) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2008.02.005 · Cited by 58
Nonlinearities in the Relation Between the Equity Risk Premium and the Term Structure
Jacob Boudoukh, A., Richardson, M. P., & Whitelaw, R. (1997) · Management Science · doi:10.1287/mnsc.43.3.371 · Cited by 57
Density-Driven Generalized Regression Neural Networks (DD-GRNN) for Function Approximation
Goulermas, J. Y., Liatsis, P., Zeng, X., & Cook, P. A. (2007) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2007.902730 · Cited by 56
One-Class Support Measure Machines for Group Anomaly DetectionPreprint
Muandet, K., & Schölkopf, B. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1303.0309 · Cited by 56
A GPU-accelerated adaptive kernel density estimation approach for efficient point pattern analysis on spatial big data
Zhang, G., Zhu, A.-X., & Huang, Q. (2017) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2017.1324975 · Cited by 55
Kernel density estimation (KDE) with adaptive bandwidth selection for environmental contours of extreme sea states
Eckert-Gallup, A. C., & Martin, N. (2016) · MTS/IEEE OCEANS Conference (OCEANS) · doi:10.1109/oceans.2016.7761150 · Cited by 54
On the Bias of Variable Bandwidth Curve Estimators
Hall, P. M. (1990) · Biometrika · doi:10.1093/biomet/77.3.529 · Cited by 54
Fast computation of spatially adaptive kernel estimates
Davies, T. M., & Baddeley, A. J. (2017) · Statistics and Computing · doi:10.1007/s11222-017-9772-4 · Cited by 53
Techniques for dealing with incomplete data: a tutorial and survey
Aste, M., Boninsegna, M., Freno, A., & Trentin, E. (2014) · Pattern Analysis and Applications · doi:10.1007/s10044-014-0411-9 · Cited by 55
Classification on Soft Labels Is Robust against Label Noise
Thiel, C. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85563-7_14 · Cited by 49
Scaling Gaussian RBF kernel width to improve SVM classification
Chang, Q., Chen, Q., & Wang, X. (2006) · International Conference on Neural Networks and Brain · doi:10.1109/icnnb.2005.1614559 · Cited by 49
NONPARAMETRIC AND SEMIPARAMETRIC METHODS FOR ECONOMIC RESEARCH
Delgado, M. A., & Robinson, P. M. (1992) · Journal of Economic Surveys · doi:10.1111/j.1467-6419.1992.tb00151.x · Cited by 49
On near neighbour estimates of a multivariate density
Hall, P. M. (1983) · Journal of Multivariate Analysis · doi:10.1016/0047-259x(83)90003-9 · Cited by 48

Authors: Breiman, L., Meisel, W., & Purcell, E.
Affiliations: Technology Service Corporation (United States) (US)
Venue: Technometrics
Publisher: Informa UK Limited
Published: 1977-05
Volume: 19
Issue: 2
Pages: 135-144
ISSN: 0040-1706 (ISSN-L)
1537-2723
DOI: 10.1080/00401706.1977.10489521
OpenAlex: W2008572210
Cited by: 488
References: 10
Citations by year: 2026: 6 · 2025: 9 · 2024: 8 · 2023: 18 · 2022: 11 · 2021: 17 · 2020: 10 · 2019: 14 · 2018: 13 · 2017: 13 · 2016: 13 · 2015: 30 · 2014: 13 · 2013: 14 · 2012: 16
FWCI: 3.51
Type: journal article
Language: en
Open access: closed
Locations: Technometrics (publishedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Distribution Estimation and Applications
Keywords: multivariate density estimation, nearest neighbor distance, finite-sample estimation

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Variable Kernel Estimates of Multivariate Densities

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks