Gaussian process (GP) regression is a flexible, nonparametric approach to regression that naturally quantifies uncertainty.
We show that the non-robustness of the Heckman model may be due to the assumption of the constant sample selection bias parameter rather than the normality assumption.
Selection bias Methodology
We use available measurements to estimate the unknown parameters (variance, smoothness parameter, and covariance length) of a covariance function by maximizing the joint Gaussian log-likelihood function.
Computation 62F99, 62P12, 62M30 G.3; G.4; J.2
We define an outlyingness matrix by extending directional outlyingness, an effective measure of the shape variation of curves that combines the direction of outlyingness with conventional depth.