In order to obtain many of the classical results in the theory of
statistical estimation, it is usual to impose regularity conditions on
the distributions under consideration. In small sample and large sample
theories of estimation there are well established sets of regularity
conditions, and it is worth while to examine what may follow if any one
of these regularity conditions fail to hold. "Non-regular estimation"
literally means the theory of statistical estimation when some or other
of the regularity conditions fail to hold. In this monograph, the
authors present a systematic study of the meaning and implications of
regularity conditions, and show how the relaxation of such conditions
can often lead to surprising conclusions. Their emphasis is on
considering small sample results and to show how pathological examples
may be considered in this broader framework.