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Fixed design local polynomial smoothing and bandwidth selection for right censored data

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  • Bagkavos, Dimitrios
  • Ioannides, Dimitrios

Abstract

The local polynomial smoothing of the Kaplan–Meier estimate for fixed designs is explored and analyzed. The first benefit, in comparison to classical convolution kernel smoothing, is the development of boundary aware estimates of the distribution function, its derivatives and integrated derivative products of any arbitrary order. The advancements proceed by developing asymptotic mean integrated square error optimal solve-the-equation plug-in bandwidth selectors for the estimates of the distribution function and its derivatives, and as a byproduct, a mean square error optimal bandwidth rule for integrated derivative products. The asymptotic properties of all methodological contributions are quantified analytically and discussed in detail. Three real data analyses illustrate the benefits of the proposed methodology in practice. Finally, numerical evidence is provided on the finite sample performance of the proposed technique with reference to benchmark estimates.

Suggested Citation

  • Bagkavos, Dimitrios & Ioannides, Dimitrios, 2021. "Fixed design local polynomial smoothing and bandwidth selection for right censored data," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).
    • Handle: RePEc:eee:csdana:v:153:y:2021:i:c:s0167947320301559
    DOI: 10.1016/j.csda.2020.107064
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    References listed on IDEAS

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    1. Hairui Hua & Prakash N. Patil & Dimitrios Bagkavos, 2017. "Semiparametric smoothing approach to hazard rate estimation," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(3), pages 669-693, July.
    2. Ming‐Yen Cheng, 1997. "Boundary Aware Estimators of Integrated Density Derivative Products," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(1), pages 191-203.
    3. McNeil, Alexander J., 1997. "Estimating the Tails of Loss Severity Distributions Using Extreme Value Theory," ASTIN Bulletin, Cambridge University Press, vol. 27(1), pages 117-137, May.
    4. Kus, Coskun, 2007. "A new lifetime distribution," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4497-4509, May.
    5. Hall, Peter, 1984. "Central limit theorem for integrated square error of multivariate nonparametric density estimators," Journal of Multivariate Analysis, Elsevier, vol. 14(1), pages 1-16, February.
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