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ALLSTAT  January 2007

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Subject:

ANNOUNCE: RSS October Ordinary Meeting

From:

Trevor Sweeting <[log in to unmask]>

Reply-To:

Trevor Sweeting <[log in to unmask]>

Date:

Fri, 19 Jan 2007 12:34:45 -0000

Content-Type:

text/plain

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text/plain (38 lines)

Ordinary meeting of the Royal Statistical Society organized by the
Research Section

Wednesday, January 31st, 2007 at 5pm (tea from 4:30pm)

Venue: Royal Statistical Society, 12 Errol St, London EC1Y 8LX


D Zeng and D Lin (University of North Carolina)

Maximum likelihood estimation in semiparametric regression models with
censored data

Semiparametric regression models play a central role in formulating the
effects of covariates on potentially censored failure times and in the
joint modelling of incomplete repeated measures and failure times in
longitudinal studies. The presence of infinite-dimensional parameters
poses considerable theoretical and computational challenges in the
statistical analysis of such models. We present several classes of
semiparametric regression models, which extend the existing models in
important directions. We construct appropriate likelihood functions
involving both finite-dimensional and infinite-dimensional parameters.
The maximum likelihood estimators are consistent and asymptotically
normal with efficient variances. We develop simple and stable numerical
techniques to implement the corresponding inference procedures.
Extensive simulation experiments demonstrate that the proposed
inferential and computational methods perform well in practical
settings. Applications to three medical studies yield important new
insights. We conclude that there is no reason, theoretical or numerical,
not to use maximum likelihood estimation for semiparametric regression
models. We discuss several areas that need further research.

You can download/view a PDF copy of this paper at

http://www.rss.org.uk/main.asp?page=1836#1788

Trevor Sweeting
Chair, RSS Research Section Committee

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