Mon. 4-5, Rm 1308
Fall 2013 and continuing Spring 2014
Eric Slud Statistics Program , Math Department Rm 2314 x5-5469
Interested participants should send email to
updated for Spring 2014 with papers for Spring 2014 highlighted by red asterisks
Schedule of Talks
updated with Slides where available
Research Focus: A great deal of current research in parametric,
semiparametric and also
sample-survey statistical inference is organized around Estimating Equations. This includes
plus many other topics. We will study papers from a few
of these areas, focusing in areas of
interest to the RIT attendees.
Graduate Student Prerequisites: To benefit from this research
activity, a graduate student
should have completed Stat 700-701 and Stat 600.
Graduate Program: Graduate students will be involved in
reading and presenting
papers from the statistical literature concerning provable properties of estimators from
Work Schedule: We will meet weekly.
Students will choose from the following list of Topics
and Papers (which will regularly be augmented on this web-page) and will present the material
in subsequent weeks, after an introductory couple of weeks' talks. Presentations can be informal,
but should be detailed enough and present enough background that we can understand
the issues and ideas clearly. Some presentations will extend to a second week.
Topics by Keyword:
Also see material on previous web-pages concerning
and statistics related to Biased Sampling.
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.
Chen, Jiahua and Qin, Jing (1993)
Empirical Likelihood Estimation for Finite Populations and the Effective
Usage of Auxiliary Information, Biometrika 80, 107-116.
V. P. Godambe classic paper on optimal estimating equations,
An Optimum Property of Regular Maximum Likelihood Estimation, pp. 1208-1211, Ann. Math. Stat. 31
Stable URL: http://links.jstor.org/sici?sici=0003-4851%28196012%2931%3A4%3C1208%3AAOPORM%3E2.0.CO%3B2-K
Godambe, V. and Thompson, M. (1986) Parameters of
Superpopulation and Survey Population:
Their Relationships and Estimation, International Statistical Review 54, 127-138.
* Heyde, C. (1997), "Quasilikelihood and its Application", Springer book.
Hirano, K., Imbens, G. and Ridder, G. (2003) Efficient estimation of average treatment
effects using the
estimated propensity score, Econometrica 71, 1161-1189.
Huber, P. (1967) classic paper on M-estimation from the 5th Berkeley Symposium,
The behavior of maximum likelihood estimates under nonstandard conditions,
Proc. Fifth Berkeley Symp. on Math. Statist. and Prob., Vol. 1 (Univ. of Calif. Press, 1967), 221-233.
Janicki, R. (2009) UMCP thesis on Estimating Equations including misspecified ones.
Lumley, T., Shaw, P. and Dai, J. (2011), Connections between
Survey Calibration Estimators and
Semiparametric Models for Incomplete Data, International Statistical Review 79, 200-220.
* Ma, Y. and
Zhu, L. (2012), "A semiparametric approach to dimension reduction",
Journal of American Statistical Association 107, 168-179.
Pfeffermann, D. and Sverchkov, M.: work on survey data with
* J. Robins papers
(many with Rotnitzky and other authors) on inverse-probability weighted
estimating equations, starting with
* Robins, J., Rotnitzky, A. and Zhao, L. (1994), Estimation of regression coefficients when some regressors
are not always observed, Jour. Amer. Statist. Assoc. 89, 846-866.
* Tan, Z.
several papers and discussions on missing data, causal inference, and double robustness, starting
* Z. Tan (2007) Understanding OR, PS, and DR, Discussion of "Demystifying double robustness:
A comparison of alternative strategies for estimating a population mean from incomplete
data" by Kang and Schafer, Statistical Science 22, 560-568.
* Tsiatis, A. (2006) book, "Semiparametric Theory and Missing Data", Springer.
* Varin, C., Reid, N. and Firth, C. (2011), "An Overview of Composite Likelihood Methods" Statistica Sinica 21, 5-42.
White, Halbert (1982) Maximum likelihood estimation of misspecified
Econometrica 50, no. 1, 1-25.
* Zeger, S., Liang, K and Albert, P. (1988) Models for longitudinal data: a generalized estimating equation approach,
Biometrics 44, 1049-1060.
Schedule of Talks ---
Talks during Spring 2014
book, especially from Chapters 4 and 7, related to Regular Asymptotically Linear estimators, estimating equations,
and influence functions in the setting of missing-data semiparametric problems of interest in this RIT.
Eric Slud will pick up where she leaves off to discuss the "influence functions" for optimal semiparametric
estimators in outcome and response-propensity models. Slides can be found here. (This material involves
Theorem statements from Chapters 8-10 of Tsiatis (2006), but after a quick statement of results, the rest of the
presentation consists of working out examples.)
an original approach he has developed, along with students, to use imputed and augmented data in
nonparametrically, from the Hirano, Imbens, and Ridder (2003) paper in the Reading List above.
propensity-score estimation in causal inference. This paper (by a student of Imbens) was cited in the
2003 Econometrica paper of Hirano, Imbens and Ridder covered last week, and we will return to the
discussion of that paper too.
paper by Angrist, Imbens and Rubin).
who will talk about her recent reading in Arthur Owen's Empirical Likelihood book, to
connect the general empirical likelihood theory to calibrated weighted estimating equation
methods, including methods based on missing data.