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Bandwidth selection in kernel density estimation: a rewiew

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  • Berwin A. TURLACH

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  • Berwin A. TURLACH, "undated". "Bandwidth selection in kernel density estimation: a rewiew," Statistic und Oekonometrie 9307, Humboldt Universitaet Berlin.
    • Handle: RePEc:wop:humbse:9307
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    1. Mammen, Enno, 1990. "A short note on optimal bandwidth selection for kernel estimators," Statistics & Probability Letters, Elsevier, vol. 9(1), pages 23-25, January.
    2. B. W. Silverman, 1982. "Kernel Density Estimation Using the Fast Fourier Transform," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(1), pages 93-99, March.
    3. PARK, Byeong U. & TURLACH, Berwin A., 1992. "Practical performance of several data driven bandwidth selectors," LIDAM Reprints CORE 1001, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Marron, J. S., 1989. "Comments on a data based bandwidth selector," Computational Statistics & Data Analysis, Elsevier, vol. 8(2), pages 155-170, July.
    5. Jones, M. C., 1991. "The roles of ISE and MISE in density estimation," Statistics & Probability Letters, Elsevier, vol. 12(1), pages 51-56, July.
    6. Kooperberg, Charles & Stone, Charles J., 1991. "A study of logspline density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 12(3), pages 327-347, November.
    7. PARK, Byeong & TURLACH, Berwin, 1992. "Practical performance of several data driven bandwidth selectors," LIDAM Discussion Papers CORE 1992005, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    8. Hall, Peter & Wand, Matthew P., 1988. "On the minimization of absolute distance in kernel density estimation," Statistics & Probability Letters, Elsevier, vol. 6(5), pages 311-314, April.
    9. M. C. Jones & H. W. Lotwick, 1984. "A Remark on Algorithm as 176. Kernel Density Estimation Using the Fast Fourier Transform," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 33(1), pages 120-122, March.
    10. Park, B. & Kim, W. & Marron, J., 1991. "Asymptotically Best Bandwidth Selectors in Kernel Density Estimation," LIDAM Discussion Papers CORE 1991054, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    11. Marron, J S, 1988. "Automatic Smoothing Parameter Selection: A Survey," Empirical Economics, Springer, vol. 13(3/4), pages 187-208.
    12. Hall, Peter & Marron, J. S., 1987. "Estimation of integrated squared density derivatives," Statistics & Probability Letters, Elsevier, vol. 6(2), pages 109-115, November.
    13. HARDLE, Wolfgang & SCOTT, David, 1990. "Smoothing by weighted averaging of rounded points," LIDAM Discussion Papers CORE 1990040, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    14. N/A, 1989. "Comments," ILR Review, Cornell University, ILR School, vol. 43(1), pages 89-102, October.
    15. Hall, Peter. & Wand, M.P., "undated". "On the Accuracy of Binned Kernel Density Estimators," Statistics Working Paper 93003, Australian Graduate School of Management.
    16. Hardle, W. & Marron, J.S. & Wand, Mp., 1990. "Bandwith choice for density derivatives," LIDAM Reprints CORE 945, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    17. Jones, M. C. & Sheather, S. J., 1991. "Using non-stochastic terms to advantage in kernel-based estimation of integrated squared density derivatives," Statistics & Probability Letters, Elsevier, vol. 11(6), pages 511-514, June.
    18. Berwin A. TURLACH:, "undated". "Discretization methods for average derivative estimation," Statistic und Oekonometrie 9306, Humboldt Universitaet Berlin.
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    Cited by:

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    2. Subbiah, Mohan & Fabozzi, Frank J., 2016. "Hedge fund allocation: Evaluating parametric and nonparametric forecasts using alternative portfolio construction techniques," International Review of Financial Analysis, Elsevier, vol. 45(C), pages 189-201.
    3. Mihály Ormos & Dávid Zibriczky, 2014. "Entropy-Based Financial Asset Pricing," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-21, December.
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    5. Vesa Hasu & Kalle Halmevaara & Heikki Koivo, 2011. "An approximation procedure of quantiles using an estimation of kernel method for quality control," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(2), pages 399-413.
    6. Wang, Qing & Lindsay, Bruce G., 2015. "Improving cross-validated bandwidth selection using subsampling-extrapolation techniques," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 51-71.
    7. Yang, Yandong & Li, Shufang & Li, Wenqi & Qu, Meijun, 2018. "Power load probability density forecasting using Gaussian process quantile regression," Applied Energy, Elsevier, vol. 213(C), pages 499-509.
    8. Ossandon Busch, Matias & Sánchez-Martínez, José Manuel & Rodríguez-Martínez, Anahí & Montañez-Enríquez, Ricardo & Martínez-Jaramillo, Serafín, 2022. "Growth at risk: Methodology and applications in an open-source platform," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 3(3).
    9. Lin, Yan-Hui & Jiao, Xin-Lei, 2021. "Adaptive Kernel Auxiliary Particle Filter Method for Degradation State Estimation," Reliability Engineering and System Safety, Elsevier, vol. 211(C).

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