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.
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