Meeting 2-3:15pm, Thursdays (regular room MTH1313)
Eric Slud, Statistics Program , Math Department
Interested participants should email to:
Schedule of Talks
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)
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
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
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.