Fridays 1-2 pm, Rm Mth 2400
Eric Slud Statistics Program , Math Department Rm 2314 x5-5469
Interested participants should send email to
Schedule of Talks
Research Focus: Semiparametric statistical theory, which is
broadly speaking about the estimation of
finite-dimensional (`structural') parameters like regression coefficients in the presence of infinite-
dimensional `nuisance ' parameters like unknown error distributions in regression or like unknown
baseline hazard functions and censoring distributions in survival regression problems.
Much of the research in this field has been done in a biostatistical setting; econometricians have also
worked heavily in it. We will cover some of the basic examples (Cox model, `accelerated failure
models' as an instance of right-censored regression models), which I hope will be presented by RIT
participants after a couple of introductory lectures. After that we will proceed according to the interests
of participants, but with at least some attention to "biased sampling" semiparametric models which
relate to survey weights.
For those that are new to the
Semiparametrics topic, the essential introductory reading is
Chapter 25 of the van der Vaart book, Sections 25.1-25.5. That is what the introductory talks
are based on. From there, the Examples will be expanded using journal papers and (for some
topics) the Tsiatis book.
Graduate-student Prerequisites: To benefit from this research
activity, a graduate student
should have completed Stat 700-701 and Stat 600-601.
Graduate Program: Graduate students will be involved in
reading and presenting
papers from the statistical literature concerning provable properties of semiparametric estimators.
Work Schedule: We will meet weekly in the spring of 2009.
Students will choose
from the following list of Topics and Papers (which will regularly be augmented on
this web-page) and present the material in subsequent weeks, after an introductory
couple of weeks' talks by me. Presentations can be informal, but should be detailed
enough and present enough background that we can understand the issues and ideas
clearly. It is expected that many presentations will extend to a second week.
Topics by Keyword:
or by a biased sampling design,
spline density estimation techniques.
(i) Bickel, P., Klaassen, C., Ritov, Y. Wellner, J. (1993),
Efficient and Adaptive Estimation for
Semiparametric Models, Johns Hopkins Univ. Press: Baltimore.
This is now re-issued as a Springer paperback, but is very difficult to read.
(ii) LeCam, L. & Yang, G. (1990), Asymptotics in Statistics: Some
Basic Concepts, Springer-Verlag: New York.
A good and readable text on contiguity theory, local asymptotic normality and applications.
(iii) Van der Vaart, A. (1998, paperback edition 2000) Asymptotic
Statistics, Cambridge Univ. Press.
This text was used in Stat 710 a couple of years ago and will be used now in introducing the semiparametrics
topic. It contains excellent introductory chapters on contiguity and empirical processes and semiparametrics.
(iv) Tsiatis, A. (2006) Semiparametric Theory and Missing Data
(Springer Series in Statistics).
(v) Hastie, T. J. and Tibshirani, R. J. (1990). Generalized Additive Models. Chapman & Hall/CRC.
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.
Godambe, V.P. and Heyde, C. 1987 ISI review paper on Quasi-likelihood and optimal estimation.
Kosorok, M., Lee, B., and Fine, J. (2004) Robust inference for
proportional hazards univariate frailty
regression models. Ann. Statist. 32, 1448-1491.
Lai, T.L. and Ying, Z. (1992) Asymptotically efficient
estimation in censored and truncated
regression models. Statistica Sinica 2, 17-46.
Li, Haihong, Lindsay, Bruce G. and Waterman, Richard P. (2003)
Efficiency of projected
score methods in rectangular array asymptotics. J. Roy. Statist. Soc. Ser. B 65, 191-208.
Lindsay, Bruce, Clogg, C., and Grego, J. (1991)
Semiparametric estimation in the Rasch
model and related exponential response models, including a simple latent class model
for item analysis. J. Amer. Statist. Assoc. 86, 96-107.
Parner, E. (1998) Asymptotic theory for the correlated gamma-frailty model. Ann. Statist. 26, 183-214.
Pfeffermann, D. and Sverchkov, M. work on survey data with
Qin, J. (1994?) Ann. Statist. papers on empirical likelihood
Rotnitzky and Robins papers (some with other co-authors) on
inverse-probability weighted estimating equations for
longitudinal studies (eg AIDS) with informative dropout patterns
Slud, E. and Vonta, I. (2005) Efficient semiparametric
estimators via modified profile likelihood. Jour. Statist.
Planning & Inference 129, 339-367.
Schedule of Talks ---
This talk contained more preliminary material from the van der Vaart book, together with some general comments
about approaches in the literature to finding, semiparametric efficient estimators. For a pdf file of slides summarizing
the talks on Jan. 30, Feb. 6, and Feb. 20, see Semiparametric Efficiency Slides.
with more examples: February 20, Eric Slud & Paul Smith.
Semiparametric Efficiency Slides linked above and conference talk about Slud-Vonta paper: March 6, Eric Slud
Ziliang continued to speak April 10, and April 17.
Last updated April 17, 2009.