🚀 go-pugleaf

Topic:    Marginal tail area approximations in multi-variable problems
Speaker:  Chris Field, Dept of Math, Stats & Comp Sci, Dalhousie University
Date:     4:00 p.m., February 10, 1989
Place:    room 2110, Sidney Smith Hall, 100 St George St, University of Toronto
Abstract:

In many statistical problems, it is important to be able to compute tail
areas accurately.  We present a tail area approximation for the mean developed
by Lugannani and Rice. We show how it can be viewed as a local normal approx-
imation after embedding the underlying density in an appropriate exponential
family.  The results are extended to multi-variable situations in which the
density of statistics is proportional to exp(1(x)) where 1(x) is known and the
distribution function of one of the statistics is to be approximated.  Condi-
tional inference about the parameters of a multiple regression problem fall
into this category.  We present the appropriate tail area approximation
required to compute conditional confidence intervals for a single parameter of
interest.  Numerical results for some common location/scale models show excel-
lent agreement between exact and approximate areas for samples of size 5 with
2 of these being type 2 censored.  Some preliminary numerical results for mul-
tiple regression will also be given.
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Coffee and tea will be served in the DeLury Lounge (SS6006) at 3:30 p.m.