Multivariate risks and depth-trimmed regions

Multivariate risks and depth-trimmed regions

We describe a general framework for measuring risks, where the risk measure takes values in an abstract cone. It is shown that this approach naturally includes the classical risk measures and set-valued risk measures and yields a natural definition of vector-valued risk measures. Several main constructions of risk measures are described in this abstract axiomatic framework. It is shown that the...

Tukey Depth-based Multivariate Trimmed Means

We investigate the asymptotic behavior of two types of Tukey depth-based multivariate trimmed means. Sufficient conditions for asymptotic normality of these location estimators are given. Two approaches to trimming are distinguished and central limit theorems are derived for each one. Asymptotic normality is proved using Hadamard differentiability of the location functionals. In the one-dimensi...

Finite Sample Tail Behavior of the Multivariate Trimmed Mean Based on Tukey-Donoho Halfspace Depth

The Multivariate Least Trimmed Squares Estimator

In this paper we introduce the least trimmed squares estimator for multivariate regression. We give three equivalent formulations of the estimator and obtain its breakdown point. A fast algorithm for its computation is proposed. We prove Fisherconsistency at the multivariate regression model with elliptically symmetric error distribution and derive the influence function. Simulations investigat...

Multivariate Spacings Based on Data Depth : I . Construction of Nonparametric Multivariate Tolerance Regions

This paper introduces and studies multivariate spacings. The spacings are developed using the order statistics derived from data depth. Specifically, the spacing between two consecutive order statistics is the region which bridges the two order statistics, in the sense that the region contains all the points whose depth values fall between the depth values of the two consecutive order statistic...