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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.
Original language | English |
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Journal | Econometric Reviews |
Volume | 36 |
Issue | 6-9 |
Pages (from-to) | 970-987 |
Number of pages | 18 |
ISSN | 0747-4938 |
DOIs | |
Publication status | Published - 2017 |
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ID: 121437124