Most dimension reduction methods based on nonparametric smoothing are highly sensitive to outliers and to data coming from heavy-tailed distributions.We show that the recently proposed methods by Xia et al.(2002) can be made robust in such a way that preserves all advantages of the original...

The Nadaraya-Watson nonparametric estimator of regression is known to be highly sensitive to the presence of outliers in data.This sensitivity can be reduced, for example, by using local L-estimates of regression.Whereas the local L-estimation is traditionally done using an empirical conditional...

Abstract: Data depth measures the centrality of a point with respect to a given distribution or data cloud. It provides a natural center-outward ordering of multivariate data points and yields a systematic nonparametric multivariate analysis scheme. In particular, the halfspace depth is shown to...

Using a novel enterprise survey from Kenya (FinAccess Business), we document a strong positive association between the use of mobile money as a method to pay suppliers and access to trade credit. We develop a dynamic general equilibrium model with heterogeneous entrepreneurs, imperfect credit...

Data depth measures the centrality of a point with respect to a given distribution or data cloud. It provides a natural center-outward ordering of multivariate data points and yields a systematic nonparametric multivariate analysis scheme. In particular, the halfspace depth is shown to have...

This paper considers the investment decision of a firm where it has to decide about the timing and capacity. We obtain that in a fast growing market, right after investment the firm produces below capacity, where the utilization rate (the proportion of capacity that is used for production right...