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Correlated data arise in numerous contexts across a wide spectrum
of subject-matter disciplines. Modeling such data present special
challenges and opportunities that have received increasing scrutiny
by the statistical community in recent years. In October 1996 a
group of 210 statisticians and other scientists assembled on the
small island of Nantucket, U. S. A. , to present and discuss new
developments relating to Modelling Longitudinal and Spatially
Correlated Data: Methods, Applications, and Future Direc tions. Its
purpose was to provide a cross-disciplinary forum to explore the
commonalities and meaningful differences in the source and
treatment of such data. This volume is a compilation of some of the
important invited and volunteered presentations made during that
conference. The three days and evenings of oral and displayed
presentations were arranged into six broad thematic areas. The
session themes, the invited speakers and the topics they addressed
were as follows: * Generalized Linear Models: Peter
McCullagh-"Residual Likelihood in Linear and Generalized Linear
Models" * Longitudinal Data Analysis: Nan Laird-"Using the General
Linear Mixed Model to Analyze Unbalanced Repeated Measures and
Longi tudinal Data" * Spatio---Temporal Processes: David R.
Brillinger-"Statistical Analy sis of the Tracks of Moving
Particles" * Spatial Data Analysis: Noel A. Cressie-"Statistical
Models for Lat tice Data" * Modelling Messy Data: Raymond J.
Carroll-"Some Results on Gen eralized Linear Mixed Models with
Measurement Error in Covariates" * Future Directions: Peter J.
Provides cutting-edge methods, specialized macros, and proven "best
bet" procedures. The specialized macros and dozens of real-world
examples illustrate solutions for a broad variety of problems that
call for multiple inferences. The book also discusses the pitfalls
and advantages of various methods, thereby helping you decide which
is the most appropriate for your purposes.
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