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This volume is the first book-length treatment of model-based
geostatistics. The text is expository, emphasizing statistical
methods and applications rather than the underlying mathematical
theory. Analyses of datasets from a range of scientific contexts
feature prominently, and simulations are used to illustrate
theoretical results. Readers can reproduce most of the
computational results in the book by using the authors' software
package, geoR, whose usage is illustrated in a computation section
at the end of each chapter. The book assumes a working knowledge of
classical and Bayesian methods of inference, linear models, and
generalized linear models.
Assembling a collection of very prominent researchers in the field,
the Handbook of Spatial Statistics presents a comprehensive
treatment of both classical and state-of-the-art aspects of this
maturing area. It takes a unified, integrated approach to the
material, providing cross-references among chapters. The handbook
begins with a historical introduction detailing the evolution of
the field. It then focuses on the three main branches of spatial
statistics: continuous spatial variation (point referenced data);
discrete spatial variation, including lattice and areal unit data;
and spatial point patterns. The book also contains a section on
space-time work as well as a section on important topics that build
upon earlier chapters. By collecting the major work in the field in
one source, along with including an extensive bibliography, this
handbook will assist future research efforts. It deftly balances
theory and application, strongly emphasizes modeling, and
introduces many real data analysis examples.
This volume is the first book-length treatment of model-based
geostatistics. The text is expository, emphasizing statistical
methods and applications rather than the underlying mathematical
theory. Analyses of datasets from a range of scientific contexts
feature prominently, and simulations are used to illustrate
theoretical results. Readers can reproduce most of the
computational results in the book by using the authors' software
package, geoR, whose usage is illustrated in a computation section
at the end of each chapter. The book assumes a working knowledge of
classical and Bayesian methods of inference, linear models, and
generalized linear models.
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.
This important text has been completely revised and expanded to become the most up-to-date and thorough professional reference text in this fast-moving area of biostatistics (medical statistics). This new edition contains an additional two chapters. The first of these discusses fully parametric models for discrete repeated measures data. The second explores statistical models for time-dependent predictors where there may be feedback between the predictor and response variables.
The first edition of Analysis for Longitudinal Data has become a
classic. Describing the statistical models and methods for the
analysis of longitudinal data, it covers both the underlying
statistical theory of each method, and its application to a range
of examples from the agricultural and biomedical sciences. The main
topics discussed are design issues, exploratory methods of
analysis, linear models for continuous data, general linear models
for discrete data, and models and methods for handling data and
missing values. Under each heading, worked examples are presented
in parallel with the methodological development, and sufficient
detail is given to enable the reader to reproduce the author's
results using the data-sets as an appendix. This second edition,
published for the first time in paperback, provides a thorough and
expanded revision of this important text. It includes two new
chapters; the first discusses fully parametric models for discrete
repeated measures data, and the second explores statistical models
for time-dependent predictors.
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