Dear All
Apologies for cross-posting.
Peter Hall will be giving a seminar at the LSE on 23rd May.
Details below and at
http://www.lse.ac.uk/depts/statistics/events_and_seminars.htm
Jeremy
Dr J Penzer
Department of Statistics, LSE
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The University of London
Joint Statistical Seminars
At 4pm on 23rd May 2000
New Theatre, London School of Economics
Peter Hall
Australian National University
LSE Centennial Professor
DATA TUNING
Data-tuning methods alter the empirical distribution so as to enhance
performance of a relatively elementary technique. The idea is to
retain the advantageous features of the simpler method, and at the
same time improve its performance in specific ways. Different
approaches to data tuning include physically altering the data (data
sharpening), reweighting or tilting the data (the biased bootstrap),
adding extra ``pseudo data'' derived from the original data, or a
combination of all three. One approach to data sharpening involves
altering the positions of data values, controlled by minimising a
measure of the total distance that the data are moved subject to a
constraint. For example, to make a point estimate more robust we
move the data in such a way as to reduce variability; to render a
nonparametric density estimate unimodal we move the data by the
least amount subject to a conventional kernel estimate, for a given
bandwidth, being unimodal; to render a regression estimate monotone
increasing we might change the values of explanatory variables; and
so on. There are many applications. Evidence is growing that
sharpening is more effective than tilting (i.e. reweighting), since
it does not reduce effective sample size.
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