Kernel density estimation (English Wikipedia)

Analysis of information sources in references of the Wikipedia article "Kernel density estimation" in English language version.

refsWebsite

Global rank English rank

21doi.org

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7semanticscholar.org

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5jstor.org

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5worldcat.org

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3psu.edu

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2repec.org

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2arxiv.org

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2wolfram.com

513^th place

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2nag.co.uk

low place

1uq.edu.au

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1illinois.edu

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1harvard.edu

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1archive.org

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1jocg.org

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1mathworks.com

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1rsc.org

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1web.archive.org

1^st place

1jakevdp.github.io

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1pydata.org

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1pypi.org

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1apache.org

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1stata.com

low place

apache.org

spark.apache.org

"Basic Statistics - RDD-based API - Spark 3.0.1 Documentation". spark.apache.org. Retrieved 2020-11-05.

archive.org

Silverman, B.W. (1986). Density Estimation for Statistics and Data Analysis. London: Chapman & Hall/CRC. p. 45. ISBN 978-0-412-24620-3.

arxiv.org

Botev, Z.I.; Grotowski, J.F.; Kroese, D.P. (2010). "Kernel density estimation via diffusion". Annals of Statistics. 38 (5): 2916–2957. arXiv:1011.2602. doi:10.1214/10-AOS799. S2CID 41350591.
Chen, Yen-Chi; Genovese, Christopher R.; Wasserman, Larry (2016). "A comprehensive approach to mode clustering". Electronic Journal of Statistics. 10 (1): 210–241. arXiv:1406.1780. doi:10.1214/15-ejs1102. ISSN 1935-7524.

doi.org

Rosenblatt, M. (1956). "Remarks on Some Nonparametric Estimates of a Density Function". The Annals of Mathematical Statistics. 27 (3): 832–837. doi:10.1214/aoms/1177728190.
Parzen, E. (1962). "On Estimation of a Probability Density Function and Mode". The Annals of Mathematical Statistics. 33 (3): 1065–1076. doi:10.1214/aoms/1177704472. JSTOR 2237880.
Piryonesi S. Madeh; El-Diraby Tamer E. (2020-06-01). "Role of Data Analytics in Infrastructure Asset Management: Overcoming Data Size and Quality Problems". Journal of Transportation Engineering, Part B: Pavements. 146 (2): 04020022. doi:10.1061/JPEODX.0000175. S2CID 216485629.
Epanechnikov, V.A. (1969). "Non-parametric estimation of a multivariate probability density". Theory of Probability and Its Applications. 14: 153–158. doi:10.1137/1114019.
Scott, D. (1979). "On optimal and data-based histograms". Biometrika. 66 (3): 605–610. doi:10.1093/biomet/66.3.605.
Park, B.U.; Marron, J.S. (1990). "Comparison of data-driven bandwidth selectors". Journal of the American Statistical Association. 85 (409): 66–72. CiteSeerX 10.1.1.154.7321. doi:10.1080/01621459.1990.10475307. JSTOR 2289526.
Cao, R.; Cuevas, A.; Manteiga, W. G. (1994). "A comparative study of several smoothing methods in density estimation". Computational Statistics and Data Analysis. 17 (2): 153–176. doi:10.1016/0167-9473(92)00066-Z.
Jones, M.C.; Marron, J.S.; Sheather, S. J. (1996). "A brief survey of bandwidth selection for density estimation". Journal of the American Statistical Association. 91 (433): 401–407. doi:10.2307/2291420. JSTOR 2291420.
Agarwal, N.; Aluru, N.R. (2010). "A data-driven stochastic collocation approach for uncertainty quantification in MEMS" (PDF). International Journal for Numerical Methods in Engineering. 83 (5): 575–597. Bibcode:2010IJNME..83..575A. doi:10.1002/nme.2844. S2CID 84834908.
Xu, X.; Yan, Z.; Xu, S. (2015). "Estimating wind speed probability distribution by diffusion-based kernel density method". Electric Power Systems Research. 121: 28–37. doi:10.1016/j.epsr.2014.11.029.
Botev, Z.I.; Grotowski, J.F.; Kroese, D.P. (2010). "Kernel density estimation via diffusion". Annals of Statistics. 38 (5): 2916–2957. arXiv:1011.2602. doi:10.1214/10-AOS799. S2CID 41350591.
Sheather, S.J.; Jones, M.C. (1991). "A reliable data-based bandwidth selection method for kernel density estimation". Journal of the Royal Statistical Society, Series B. 53 (3): 683–690. doi:10.1111/j.2517-6161.1991.tb01857.x. JSTOR 2345597.
Bowman, A.W. (1984). "An alternative method of cross-validation for the smoothing of density estimates". Biometrika. 71 (2): 353–360. doi:10.1093/biomet/71.2.353.
Hall, P.; Marron, J.S.; Park, B.U. (1992). "Smoothed cross-validation". Probability Theory and Related Fields. 92: 1–20. doi:10.1007/BF01205233. S2CID 121181481.
Wahba, G. (1975). "Optimal convergence properties of variable knot, kernel, and orthogonal series methods for density estimation". Annals of Statistics. 3 (1): 15–29. doi:10.1214/aos/1176342997.
Buch-Larsen, TINE (2005). "Kernel density estimation for heavy-tailed distributions using the Champernowne transformation". Statistics. 39 (6): 503–518. CiteSeerX 10.1.1.457.1544. doi:10.1080/02331880500439782. S2CID 219697435.
Chen, Yen-Chi; Genovese, Christopher R.; Wasserman, Larry (2016). "A comprehensive approach to mode clustering". Electronic Journal of Statistics. 10 (1): 210–241. arXiv:1406.1780. doi:10.1214/15-ejs1102. ISSN 1935-7524.
Chazal, Frédéric; Fasy, Brittany Terese; Lecci, Fabrizio; Rinaldo, Alessandro; Wasserman, Larry (2014). "Stochastic Convergence of Persistence Landscapes and Silhouettes". Proceedings of the thirtieth annual symposium on Computational geometry. Vol. 6. New York, New York, USA: ACM Press. pp. 474–483. doi:10.1145/2582112.2582128. ISBN 978-1-4503-2594-3. S2CID 6029340.
Fukunaga, K.; Hostetler, L. (January 1975). "The estimation of the gradient of a density function, with applications in pattern recognition". IEEE Transactions on Information Theory. 21 (1): 32–40. doi:10.1109/tit.1975.1055330. ISSN 0018-9448.
Yizong Cheng (1995). "Mean shift, mode seeking, and clustering". IEEE Transactions on Pattern Analysis and Machine Intelligence. 17 (8): 790–799. CiteSeerX 10.1.1.510.1222. doi:10.1109/34.400568. ISSN 0162-8828.
Comaniciu, D.; Meer, P. (May 2002). "Mean shift: a robust approach toward feature space analysis". IEEE Transactions on Pattern Analysis and Machine Intelligence. 24 (5): 603–619. doi:10.1109/34.1000236. ISSN 0162-8828. S2CID 691081.

harvard.edu

ui.adsabs.harvard.edu

Agarwal, N.; Aluru, N.R. (2010). "A data-driven stochastic collocation approach for uncertainty quantification in MEMS" (PDF). International Journal for Numerical Methods in Engineering. 83 (5): 575–597. Bibcode:2010IJNME..83..575A. doi:10.1002/nme.2844. S2CID 84834908.

illinois.edu

webhost.engr.illinois.edu

Agarwal, N.; Aluru, N.R. (2010). "A data-driven stochastic collocation approach for uncertainty quantification in MEMS" (PDF). International Journal for Numerical Methods in Engineering. 83 (5): 575–597. Bibcode:2010IJNME..83..575A. doi:10.1002/nme.2844. S2CID 84834908.

jakevdp.github.io

Vanderplas, Jake (2013-12-01). "Kernel Density Estimation in Python". Retrieved 2014-03-12.

jocg.org

Chazal, Frédéric; Fasy, Brittany Terese; Lecci, Fabrizio; Rinaldo, Alessandro; Wasserman, Larry (2014). "Stochastic Convergence of Persistence Landscapes and Silhouettes". Proceedings of the thirtieth annual symposium on Computational geometry. Vol. 6. New York, New York, USA: ACM Press. pp. 474–483. doi:10.1145/2582112.2582128. ISBN 978-1-4503-2594-3. S2CID 6029340.

jstor.org

Parzen, E. (1962). "On Estimation of a Probability Density Function and Mode". The Annals of Mathematical Statistics. 33 (3): 1065–1076. doi:10.1214/aoms/1177704472. JSTOR 2237880.
Park, B.U.; Marron, J.S. (1990). "Comparison of data-driven bandwidth selectors". Journal of the American Statistical Association. 85 (409): 66–72. CiteSeerX 10.1.1.154.7321. doi:10.1080/01621459.1990.10475307. JSTOR 2289526.
Jones, M.C.; Marron, J.S.; Sheather, S. J. (1996). "A brief survey of bandwidth selection for density estimation". Journal of the American Statistical Association. 91 (433): 401–407. doi:10.2307/2291420. JSTOR 2291420.
Sheather, S.J.; Jones, M.C. (1991). "A reliable data-based bandwidth selection method for kernel density estimation". Journal of the Royal Statistical Society, Series B. 53 (3): 683–690. doi:10.1111/j.2517-6161.1991.tb01857.x. JSTOR 2345597.
Rudemo, M. (1982). "Empirical choice of histograms and kernel density estimators". Scandinavian Journal of Statistics. 9 (2): 65–78. JSTOR 4615859.

mathworks.com

"Kernel smoothing function estimate for univariate and bivariate data - MATLAB ksdensity". www.mathworks.com. Retrieved 2020-11-05.

nag.co.uk

The Numerical Algorithms Group. "NAG Library Routine Document: nagf_smooth_kerndens_gauss (g10baf)" (PDF). NAG Library Manual, Mark 23. Retrieved 2012-02-16.
The Numerical Algorithms Group. "NAG Library Routine Document: nag_kernel_density_estim (g10bac)" (PDF). NAG Library Manual, Mark 9. Archived from the original (PDF) on 2011-11-24. Retrieved 2012-02-16.

psu.edu

citeseerx.ist.psu.edu

Park, B.U.; Marron, J.S. (1990). "Comparison of data-driven bandwidth selectors". Journal of the American Statistical Association. 85 (409): 66–72. CiteSeerX 10.1.1.154.7321. doi:10.1080/01621459.1990.10475307. JSTOR 2289526.
Buch-Larsen, TINE (2005). "Kernel density estimation for heavy-tailed distributions using the Champernowne transformation". Statistics. 39 (6): 503–518. CiteSeerX 10.1.1.457.1544. doi:10.1080/02331880500439782. S2CID 219697435.
Yizong Cheng (1995). "Mean shift, mode seeking, and clustering". IEEE Transactions on Pattern Analysis and Machine Intelligence. 17 (8): 790–799. CiteSeerX 10.1.1.510.1222. doi:10.1109/34.400568. ISSN 0162-8828.

pydata.org

seaborn.pydata.org

"seaborn.kdeplot — seaborn 0.10.1 documentation". seaborn.pydata.org. Retrieved 2020-05-12.

pypi.org

"Kde-gpu: We implemented nadaraya waston kernel density and kernel conditional probability estimator using cuda through cupy. It is much faster than cpu version but it requires GPU with high memory".

repec.org

ideas.repec.org

Park, B.U.; Turlach, B.A. (1992). "Practical performance of several data driven bandwidth selectors (with discussion)". Computational Statistics. 7: 251–270.
Jann, Ben (2008-05-26), "KDENS: Stata module for univariate kernel density estimation", Statistical Software Components, Boston College Department of Economics, retrieved 2022-10-15

rsc.org

"Software for calculating kernel densities". www.rsc.org. Retrieved 2020-11-05.

semanticscholar.org

api.semanticscholar.org

Piryonesi S. Madeh; El-Diraby Tamer E. (2020-06-01). "Role of Data Analytics in Infrastructure Asset Management: Overcoming Data Size and Quality Problems". Journal of Transportation Engineering, Part B: Pavements. 146 (2): 04020022. doi:10.1061/JPEODX.0000175. S2CID 216485629.
Agarwal, N.; Aluru, N.R. (2010). "A data-driven stochastic collocation approach for uncertainty quantification in MEMS" (PDF). International Journal for Numerical Methods in Engineering. 83 (5): 575–597. Bibcode:2010IJNME..83..575A. doi:10.1002/nme.2844. S2CID 84834908.
Botev, Z.I.; Grotowski, J.F.; Kroese, D.P. (2010). "Kernel density estimation via diffusion". Annals of Statistics. 38 (5): 2916–2957. arXiv:1011.2602. doi:10.1214/10-AOS799. S2CID 41350591.
Hall, P.; Marron, J.S.; Park, B.U. (1992). "Smoothed cross-validation". Probability Theory and Related Fields. 92: 1–20. doi:10.1007/BF01205233. S2CID 121181481.
Buch-Larsen, TINE (2005). "Kernel density estimation for heavy-tailed distributions using the Champernowne transformation". Statistics. 39 (6): 503–518. CiteSeerX 10.1.1.457.1544. doi:10.1080/02331880500439782. S2CID 219697435.
Chazal, Frédéric; Fasy, Brittany Terese; Lecci, Fabrizio; Rinaldo, Alessandro; Wasserman, Larry (2014). "Stochastic Convergence of Persistence Landscapes and Silhouettes". Proceedings of the thirtieth annual symposium on Computational geometry. Vol. 6. New York, New York, USA: ACM Press. pp. 474–483. doi:10.1145/2582112.2582128. ISBN 978-1-4503-2594-3. S2CID 6029340.
Comaniciu, D.; Meer, P. (May 2002). "Mean shift: a robust approach toward feature space analysis". IEEE Transactions on Pattern Analysis and Machine Intelligence. 24 (5): 603–619. doi:10.1109/34.1000236. ISSN 0162-8828. S2CID 691081.

stata.com

"kdensity — Univariate kernel density estimation" (PDF). Stata 15 manual.

uq.edu.au

espace.library.uq.edu.au

Botev, Zdravko (2007). Nonparametric Density Estimation via Diffusion Mixing (Technical report). University of Queensland.

web.archive.org

The Numerical Algorithms Group. "NAG Library Routine Document: nag_kernel_density_estim (g10bac)" (PDF). NAG Library Manual, Mark 9. Archived from the original (PDF) on 2011-11-24. Retrieved 2012-02-16.

wolfram.com

reference.wolfram.com

"SmoothKernelDistribution—Wolfram Language Documentation". reference.wolfram.com. Retrieved 2020-11-05.
"KernelMixtureDistribution—Wolfram Language Documentation". reference.wolfram.com. Retrieved 2020-11-05.

worldcat.org

Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome H. (2001). The Elements of Statistical Learning : Data Mining, Inference, and Prediction : with 200 full-color illustrations. New York: Springer. ISBN 0-387-95284-5. OCLC 46809224.
Chen, Yen-Chi; Genovese, Christopher R.; Wasserman, Larry (2016). "A comprehensive approach to mode clustering". Electronic Journal of Statistics. 10 (1): 210–241. arXiv:1406.1780. doi:10.1214/15-ejs1102. ISSN 1935-7524.
Fukunaga, K.; Hostetler, L. (January 1975). "The estimation of the gradient of a density function, with applications in pattern recognition". IEEE Transactions on Information Theory. 21 (1): 32–40. doi:10.1109/tit.1975.1055330. ISSN 0018-9448.
Yizong Cheng (1995). "Mean shift, mode seeking, and clustering". IEEE Transactions on Pattern Analysis and Machine Intelligence. 17 (8): 790–799. CiteSeerX 10.1.1.510.1222. doi:10.1109/34.400568. ISSN 0162-8828.
Comaniciu, D.; Meer, P. (May 2002). "Mean shift: a robust approach toward feature space analysis". IEEE Transactions on Pattern Analysis and Machine Intelligence. 24 (5): 603–619. doi:10.1109/34.1000236. ISSN 0162-8828. S2CID 691081.