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Cross-validated mixed-datatype bandwidth selection for nonparametric cumulative distribution/survivor functions
Authors:Cong Li  Hongjun Li
Institution:1. School of Economics, Shanghai University of Finance and Economics, Shanghai, P. R. China;2. International School of Economics and Management, Capital University of Economics and Business, Beijing, PR China
Abstract: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.
Keywords:Bandwidth selection  Kolmogorov-Smirnov test  least square cross-validation  mixed-data
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