This book introduces a new way to analyze multivariate data. The analysis of data
based on multivariate spatial signs and ranks proceeds very much as does a traditional
multivariate analysis relying on the assumption of multivariate normality: the
L2 norm is just replaced by different L1 norms, observation vectors are replaced by
their (standardized and centered) spatial signs and ranks, and so on. Themethods are
fairly efficient and robust, and no moment assumptions are needed. A unified theory
starting with the simple one-sample location problem and proceeding through
the several-sample location problems to the general multivariate linear regression
model and finally to the analysis of cluster-dependent data is presented.