When comparing two competing approximate models using a particular loss function, the one having smallest `expected true error' for that loss function is expected to lie closest to the underlying data generating process (DGP) given this loss function and is therefore to be preferred. In this...

Nonparametric smoothing under shape constraints has recently received much well-deserved attention. Powerful methods have been proposed for imposing a single shape constraint such as monotonicity and concavity on univariate functions. In this paper, we extend the monotone kernel regression...

We propose a data-driven least squares 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,...