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Making a non-parametric density estimator more attractive, and more accurate, by data perturbation

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  • Hassan Doosti
  • Peter Hall

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  • Hassan Doosti & Peter Hall, 2016. "Making a non-parametric density estimator more attractive, and more accurate, by data perturbation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(2), pages 445-462, March.
    • Handle: RePEc:bla:jorssb:v:78:y:2016:i:2:p:445-462
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    File URL: http://hdl.handle.net/10.1111/rssb.12120
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    References listed on IDEAS

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    1. Wolters, Mark A., 2012. "A Greedy Algorithm for Unimodal Kernel Density Estimation by Data Sharpening," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 47(i06).
    2. Politis, Dimitris N., 2011. "Higher-Order Accurate, Positive Semidefinite Estimation Of Large-Sample Covariance And Spectral Density Matrices," Econometric Theory, Cambridge University Press, vol. 27(4), pages 703-744, August.
    3. Carroll, Raymond J. & Delaigle, Aurore & Hall, Peter, 2011. "Testing and Estimating Shape-Constrained Nonparametric Density and Regression in the Presence of Measurement Error," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 191-202.
    4. P. Hall & B. Presnell, 1999. "Intentionally biased bootstrap methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 143-158.
    5. Politis, Dimitris N. & Romano, Joseph P., 1999. "Multivariate Density Estimation with General Flat-Top Kernels of Infinite Order," Journal of Multivariate Analysis, Elsevier, vol. 68(1), pages 1-25, January.
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    Cited by:

    1. Braun, W. John & Stafford, James & Brown, Patrick, 2020. "Data sharpening via firth’s adjusted score function," Statistics & Probability Letters, Elsevier, vol. 165(C).
    2. Nguyen, Bao Hoang & Simar, Léopold & Zelenyuk, Valentin, 2021. "Data sharpening for improving CLT approximations for DEA-type efficiency estimators," LIDAM Discussion Papers ISBA 2021033, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    3. Nguyen, Bao Hoang & Simar, Léopold & Zelenyuk, Valentin, 2022. "Data sharpening for improving central limit theorem approximations for data envelopment analysis–type efficiency estimators," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1469-1480.

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