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
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.
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“When a kernel estimator uses θ K θ Q , it is known as a balloon estimator [14].Papers such as Breiman et al. [10] have even used θ Q as a function of x ′ , known as sample-adaptive smoothing.”
From Learning to (Learn at Test Time): RNNs with Expressive Hidden States (2024) · cites this paper as Breiman et al. [10]
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Cites
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
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.
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Thao, N. T., Bois, P., & Alva, J. A. V. (1993) · Atmospheric Research · doi:10.1016/0169-8095(93)90052-p · Cited by 4
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Bowles, D. S., Bingham, G. E., Lall, U., Tarboton, D. G., Al-Adhami, M., Jensen, D. T., & McCurdy, G. D. (1990) · Digital Commons - USU (Utah State University) · Cited by 3
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Ralescu, S. S. (1993) · Journal of Statistical Planning and Inference · doi:10.1016/0378-3758(93)90041-4 · Cited by 3
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Acosta, F. M. A. (1996) · Signal Processing · doi:10.1016/s0165-1684(96)00136-3 · Cited by 3
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Comon, P., & Cheneval, Y. (1995) · Lecture notes in computer science · doi:10.1007/3-540-59497-3_290 · Cited by 2
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Choi, S. C. (1986) · Elsevier eBooks · doi:10.1016/b978-0-08-034000-5.50005-8 · Cited by 2
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Devroye, L., & Lugosi, G. (2001) · Springer series in statistics · doi:10.1007/978-1-4613-0125-7_9 · Cited by 2
Plataniotis, K. N., & Venetsanopoulos, A. N. (2000) · Digital signal processing · doi:10.1007/978-3-662-04186-4_3 · Cited by 2
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Taylor, J. (2012) · Durham e-Theses (Durham University) · Cited by 2
Raekelboom, S., & Hulle, M. M. V. (1998) · Neural Processing Letters · doi:10.1023/a:1009652814885 · Cited by 2
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Oyang, Y., Chang, D. T.-H., Ou, Y., Hung, H.-G., Wu, C.-P., & Chen, C. (2007) · arXiv (Cornell University) · doi:10.48550/arxiv.0709.2760 · Cited by 2
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Engel, J., & Gasser, T. (1995) · Journal of nonparametric statistics · doi:10.1080/10485259508832624 · Cited by 2
Essays on Semiparametric Estimation.
Lee, K.-S. (2001) · doi:10.31390/gradschool_disstheses.413 · Cited by 2
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Hoseini, A. A., & Masdari, M. (2004) · AIAA Aerospace Sciences Meeting and Exhibit · doi:10.2514/6.2004-727 · Cited by 1
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Campbell, L., & Purviance, J. E. (1992) · NASA Technical Reports Server (NASA) · Cited by 1
SELECTED PROBLEMS IN THE ANALYSIS OF NONSTATIONARY & NONLINEAR TIME SERIES
Nachane, D. M. (2011) · RePEc: Research Papers in Economics · Cited by 1
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Li, Y. (2010) · Research Repository (Delft University of Technology) · Cited by 1
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Signorini, D. F. (1998) · Open Research Online (The Open University) · doi:10.21954/ou.ro.00004dd3 · Cited by 1
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Yu, J. (2014) · Wiley StatsRef: Statistics Reference Online · doi:10.1002/9781118445112.stat02717 · Cited by 1
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Jones, M. C., & Hössjer, O. (1996) · Journal of nonparametric statistics · doi:10.1080/10485259608832686 · Cited by 1
A method for the choice of smoothing parameter
Tseng, C.-T., & Moret, B. M. E. (1990) · Mathematical and Computer Modelling · doi:10.1016/0895-7177(90)90074-w · Cited by 1
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Tarter, M. E., Freeman, W. R., & Polissar, L. (1990) · Journal of the American Statistical Association · doi:10.1080/01621459.1990.10475303 · Cited by 1
A nonparametric approach to analyzing large human populations
Cerrito, P. B. (1991) · Mathematical Biosciences · doi:10.1016/0025-5564(91)90038-k · Cited by 1
Amatulli, G., Lovreglio, R., & Leone, V. (2005) · L’Italia forestale e montana · doi:10.36253/ifm-340 · Cited by 1
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Pourtakdoust, S. H., Jalali, M. A., Ghorbani, R., & Zahedi, A. (2002) · AIAA Guidance, Navigation, and Control Conference and Exhibit · doi:10.2514/6.2002-4663 · Cited by 1
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Liao, H. (2015) · doi:10.17760/d20194104 · Cited by 1
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Victor, N. (1981) · Lecture notes in medical informatics · doi:10.1007/978-3-642-93159-8_11
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Peddemors, A. (2009) · Data Archiving and Networked Services (DANS)
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Silva, A. P. D., Stam, A., & Neter, J. (1997) · RePEc: Research Papers in Economics
Metadata
- 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
- 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
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