Please publicize the following meeting:
Announcement of an ordinary meeting of the Royal Statistical Society
organized by the Research Section
Wednesday, February 2nd, 2005, 5PM (tea from 4:30)
12 Errol St, London WC1Y 8LX
J. Copas (University of Warwick, Coventry) and
S. Eguchi (Institute of Statistical Mathematics, Tokyo)
Local model uncertainty and incomplete-data bias
Problems of the analysis of data with incomplete observations are all
too familiar in statistics. They are doubly difficult if we are also
uncertain about the choice of model. We propose a general formulation
for the discussion of such problems and develop approximations to the
resulting bias of maximum likelihood estimates on the assumption that
model departures are small. Loss of efficiency in parameter estimation
due to incompleteness in the data has a dual interpretation: the
increase in variance when an assumed model is correct; the bias in
estimation when the model is incorrect. Examples include non-ignorable
missing data, hidden confounders in observational studies and
publication bias in meta-analysis. Doubling variances before
calculating confidence intervals or test statistics is suggested as a
crude way of addressing the possibility of undetectably small
departures from the model. The problem of assessing the risk of lung
cancer from passive smoking is used as a motivating example.
http://www.rss.org.uk/main.asp?page=1836#1254
Thank you.
Professor John T. Kent, Chairman, Research Section, RSS
Department of Statistics
University of Leeds
Leeds LS2 9JT, England
e-mail: [log in to unmask]
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