Statistical depth functions are being formulated ad hoc with
increasing popularity in nonparametric inference for multivariate data. Here we
introduce several general structures for depth functions, classify many
existing examples as special cases, and establish results on the possession, or
lack thereof, of four key properties desirable for depth functions in general.
Roughly speaking, these properties may be described as: affine invariance,
maximality at center, monotonicity relative to deepest point, and vanishing at
infinity. This provides a more systematic basis for selection of a depth
function. In particular, from these and other considerations it is found that
the halfspace depth behaves very well overall in comparison with various
competitors.