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Cross-validated mixed-datatype bandwidth selection for nonparametric cumulative distribution/survivor functions

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  • Cong Li
  • Hongjun Li
  • Jeffrey S. Racine

Abstract

We propose a computationally efficient data-driven least square cross-validation method to optimally select smoothing parameters for the nonparametric estimation of cumulative distribution/survivor functions. We allow for general multivariate covariates that can be continuous, discrete/ordered categorical or a mix of either. We provide asymptotic analysis, examine finite-sample properties through Monte Carlo simulation, and consider an illustration involving nonparametric copula modeling. We also demonstrate how the approach can also be used to construct a smooth Kolmogorov–Smirnov test that has a slightly better power profile than its nonsmooth counterpart.

Suggested Citation

  • Cong Li & Hongjun Li & Jeffrey S. Racine, 2017. "Cross-validated mixed-datatype bandwidth selection for nonparametric cumulative distribution/survivor functions," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 970-987, October.
    • Handle: RePEc:taf:emetrv:v:36:y:2017:i:6-9:p:970-987
    DOI: 10.1080/07474938.2017.1307900
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    Cited by:

    1. Jeffrey Racine, 2015. "Mixed data kernel copulas," Empirical Economics, Springer, vol. 48(1), pages 37-59, February.

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