Meeting 2-3:15pm, Thursdays (regular room MTH1313)
Fall 2010
Eric Slud, Statistics Program , Math Department
Interested participants should email to:
evs@math.umd.edu
RIT Focus: Biased Sampling generally refers to
the statistical analysis of data such that the population
on which we
see data differs (in ways which we either know or model) from the
target population. This
topic is closely related to the unequal
probability sampling strategies in Sample Surveys, and to the still
more unequal probabilities with which selected units in the population
respond (i.e., provide data). This
kind of differentially missing data
is in turn closely related to notions of `censoring' in
biostatistical
studies. Unequal probabilities of sampling in
biostatistical contexts arise in connection with `prevalent
cohort' and other epidemiologic cross-sectional sampling
strategies. When biostatistical studies have
entry criteria related to the previous occurrence of some symptoms or
other biological condition (such
as being `infected' or having a
disease advanced to a specified stage), we have biased sampling.
We will read papers and
background texts concerning sampling designs with unequal
mechanisms
of selection, unequal probabilities of response,
parametric and nonparametric identifiability and analysis
of
data. The statistical machinery will involve some discussion of
Estimating Equations, semiparametric
statistics, and some
histoical discussion on the attempts that have been made to connect
survey data to
the Likelihood concept.
Prerequisites: Participants should have had a course
in Mathematical Statistics (at the level of
Stat 700-701 or higher)
and some introduction to survey or biostatistical (survival) data.
Topics by Keyword:
to Horvitz-Thompson survey estimator
Reading List (Still under construction)
Books
Fitzmaurice, G., Davidian, M., Verbeke, G. and
Molenberghs, G. eds. (2008) Longitudinal Data Analysis,
Handbooks of Modern Statistical
Methods, Chapman & Hall/CRC.
Korn, E. and Graubard, B. (1999) Analysis of Health Surveys, Wiley.
Little, R. and Rubin, D. (2002, 2nd ed.) Statistics of Missing
Data, Wiley.
Tsiatis, A. (2006) Semiparametric Theory and Missing Data
(Springer Series in Statistics).
For a current list of very useful
references related to sample survey theory,
compiled by Mikhail
Sverchkov of Bureau of Labor Statistics, click here.
Miscellaneous Papers & Reports
Addona, V. and Wolfson, DB. (2006). A formal test for the stationarity of
the incidence rate using data
from a prevalent cohort study with follow-up. Lifetime Data Analysis.
Asgharian, M., Wolfson, DB. and Zhang, X. (2006). Checking
stationarity of the incidence rate using
prevalent cohort survival
data. Statistics in Medicine.
Chen, Jinbo and Norman Breslow (2004), Semiparametric efficient
estimation for the auxiliary
outcome problem with the conditional mean model,
Canad. Jour. Statist. 32, 1-14. Click here for pdf.
Gilbert, Peter B. (2000) Large sample theory of maximum likelihood
estimates in semiparametric
biased sampling models. Ann. Statist. 28, 151--194.
Huang Y, Wang MC. (1995), Estimating the occurrence rate for
prevalent survival data in competing
risks models. Journal of the
American Statistical Association 80,1406-1415.
Kang, J. and Schafer, J.L. (2007), Demystifying Double
Robustness: A Comparison of
Alternative Strategies for Estimating a Population Mean from
Incomplete Data, Statist. Sci. 22, 523-539.
Korn, E. and Graubard, B. (2003) Estimating variance components
by using survey data.,
J. R. Stat. Soc. Ser. B 65, 175--190.
Mandel, M. and Fluss, R. (2009) Nonparametric estimation of the
probability of illness in the
illness-death model under
cross-sectional sampling. Biometrika 96, 861-872.
Patil, G. P. and Rao, C. R. (1978). Weighted distributions and
size-biased sampling with applications
to wildlife populations and
human families. Biometrics 34 179-189.
Pfeffermann, D. and Sverchkov, M. work on survey data with
semiparametrically modelled
informative nonresponse.
Qin, J. (1994ff) Ann. Statist. papers on empirical likelihood.
Rao, JNK and Wu, C. (2009), Bayesian pseudo-empirical-likelihood
intervals for complex surveys,
J. R. Stat. Soc. Ser. B 72, 533--544.
Rotnitzky and Robins papers (some with other co-authors) on
inverse-probability weighted estimating
equations for
longitudinal studies (eg AIDS) with informative dropout patterns.
Donald Rubin papers (with P. Rosenbaum and others) on Propensity Scores.
Yehuda Vardi papers (referenced in Gilbert paper above) on
nonparametric estimation of an
underlying distribution function
in a biased-sampling setting.
Schedule of Talks ---
Annals of Statistics paper, on nonparametric estimation under
length-biased sampling.
on "A paradox concerning nuisance parameters
and projected estimating functions" which is
related to ratio estimation in survey sampling but is primarily about
estimating equations.
or home page
of the same name) and how to use it in biased sampling problems.
on empirical likelihoods in survey sampling.
sampling is noninformative (ie not dependent on the measured attribute
of interest).
the area of `informative' sampling, using papers of J. Beaumont
(2008) and Sverchkov and
Pfeffermann (2004). [For precise references,
see the bibliography document on
Survey Sampling linked within the Reading
List above.]
at the RIT in MTH 1313 a 20-minute presentation on research problems
and opportunities
for collaboration in his NIH Branch.
This presentation will immediately precede Dr. Albert's 3:30pm Statistics
Seminar.
Prevalent Survival Data in Competing Risks Models.
estimation in the illness-death model from prevalent cohorts.
in time of prevalent cohorts, from
papers (listed above) of Addona and Wolfson (2006) and
Asgharian, Wolfson, and Zhang (2006).
Last updated November 1, 2010.