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

Cong Li, Hongjun Li and Jeffrey Racine

Econometric Reviews, 2017, vol. 36, issue 6-9, 970-987

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.

Date: 2017
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DOI: 10.1080/07474938.2017.1307900

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