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Colloquium Series, Department of Statistics, University of Toronto

Topic:   Jackknife Variance Estimation
Speaker: Jun Shao, University of Waterloo
Date:    Friday, April 7, 1989   3:00 p.m.
Place:   Room 2110, Sidney Smith Hall, 100 St. George St., University of Toronto
Abstract:

The traditional delete-1 jackknife variance estimator is asymtotically correct
for smooth point estimators but is inconsistent for non-smooth estimators.
This deficiency can be rectified by using a more general jackknife with d, the
number of observations deleted, depending on a smoothness measure of the point
estimator.  Our general theory explains why and when jackknife works or fails.
It also shows that for non-smooth estimators such as the sample quantiles, d has
to go to infinity at a certain rate. Two methods for efficient computation of the
delete-d jackknife variance estimators are studied.  The robustness of the jack-
knife against heavy tails of the population and error heteroscedasticity is
also discussed.
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Coffee and tea will be served in the De Lury Lounge (SS6006) at 2:30 p.m.