Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We propose a data-driven least-square cross-validation method to optimally select smoothing parameters for the nonparametric estimation of conditional cumulative distribution functions and conditional quantile functions. We allow for general multivariate covariates that can be continuous, categorical, or a mix of either. We provide asymptotic analysis, examine finite-sample properties via Monte Carlo simulation, and consider an application involving testing for first-order stochastic dominance of children’s health conditional on parental education and income. This article has supplementary materials online.