in Surveys and Biostatistics

**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
**evs@math.umd.edu**

Reading list
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

survey sampling and other contexts where there is missing data, biased sampling, or nonresponse;

incorporating auxiliary information into estimation;

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

Estimating
Equations.

**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
Semiparametric Satistics 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.

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
semiparametrically modelled

informative nonresponse.

* 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.

* Tan, Z.
several papers and discussions on missing data, causal inference, and double robustness, starting
with:

* 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
models.*

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.

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

statistical inference.

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

connect the general empirical likelihood theory to calibrated weighted estimating equation

methods, including methods based on missing data.

Dimension Reduction

and their relation to the estimating equation topics we have studied.

here in form including Nov.18 presentation.

Survey Missing-Data problems and Estimating Equations.

will be important throughout the RIT.

consistent estimation of parameters or testing within them.

conflict will be held earlier in the afternoon.

Jong Jun Lee will speak about Chapter 3 of the Tsiatis book, Semiparametric Theory and Missing Data.

estimating equations topic, primarily the J. Chen and J. Qin Biometrika paper linked above (in the Reading

list) via JSTOR.

Paul Smith will discuss the

calibration estimators, using as source the 1992 JASA paper of J.-C. Deville and C.-E. Sarndal.

information and how the form of such results might be made to relate to survey-nonresponse/calibration problems

under superpopulation large-sample asymptotics. The most relevant readings are Chapters 4 and 6 of the Tsiatis

book along with the Chen and Qin empirical-likelihood paper we head about on 10/21 and the Deville and Sarndal

survey calibration paper we heard about on 10/28.

have been talking about, with the goal of introducing the research setting of the Z. Tan papers.

Slides for the lecture are available here.

the reading list, along with the related ideas in the Z. Tan discussion of that paper.

© Last updated April 29, 2014.