January 2004 | Hardback | 300 pages 140 tables
72 figures |
ISBN: 0-521-81407-3
Bringing together a collection of previously
published work, this
book provides a timely discussion of major
considerations
relating to the construction of econometric
models that work well
to explain economic phenomena, predict future
outcomes and be
useful for policy-making. Analytical relations
between dynamic
econometric structural models and empirical
time series MVARMA,
VAR, transfer function, and univariate ARIMA
models are
established with important application for
model-checking and
model construction. The theory and applications
of these
procedures to a variety of econometric modeling
and forecasting
problems as well as Bayesian and non-Bayesian
testing, shrinkage
estimation and forecasting procedures are
also presented and
applied. Finally, attention is focused on
the effects of
disaggregation on forecasting precision and
the new Marshallian
Macroeconomic Model that features demand,
supply and entry
equations for major sectors of economies
is analysed and
described. This volume will prove invaluable
to professionals,
academics and students alike.
Contributors
A. Zellner, F. C. Palm, P. K. Trivedi, P.
Evans, C. I. Plosser, R.
I. Webb, F. W. Ahking, S. M. Miller, A. Maravall,
A. Mathis, A.
Garcia-Ferrer, R. A. Highfield, C. Hong,
G. M. Gulati, C. Min, A.
J. Hoogstrate, G. A. Pfann, J. P. LeSage,
M. Magura, J. Tobias, B.
Chen
December 2003 | Hardback | 200 pages 50 line
diagrams | ISBN:
0-521-83268-3
Certain constrained combinatorial optimisation
problems have a
natural analogue in the continuous setting
of the classical
isoperimetric problem. The study of so called
combinatorial
isoperimetric problems exploits similarities
between these two,
seemingly disparate, settings. This text
focuses on global
methods. This means that morphisms, typically
arising from
symmetry or direct product decomposition,
are employed to
transform new problems into more restricted
and easily solvable
settings whilst preserving essential structure.
This book is
based on Professor Harperfs many years experience
in teaching
this subject and is ideal for graduate students
entering the
field. The author has increased the utility
of the text for
teaching by including worked examples, exercises
and material
about applications to computer science. Applied
systematically,
the global point of view can lead to surprising
insights and
results and established researchers will
find this to be a
valuable reference work on an innovative
method for problem
solving.
Contents
1. The edge-isoperimetric problem; 2. The
minimum path problem; 3.
Stabilization and compression; 4. The vertex-isoperimetric
problem; 5. Stronger stabilization; 6. Higher
compression; 7.
Isoperimetric problems on infinite graphs;
8. Isoperimetric
problems on complexes; 9. Morphisms for MWI
problems; 10. Passage
to the limit; 11. Afterword; 12. The classical
isoperimetric
problem.
Series: Texts in Statistical Science Series
Volume: 58
ISBN: 1-58488-433-9
Publication Date: 8/15/2003
Number of Pages: 352
Assumes a fairly low level of mathematical
understanding, but
goes beyond the 'cookbook' approach
Uses a case-study approach throughout, in
order to enhance
understanding
Provides all the basic ideas for epidemiological
studies, making
the book highly useful as a text
Aims to develop the reader's feel for why
certain methods are
needed and used, and when they make a difference
Provides datasets, solutions to the exercises,
and Stata code on
a supporting Web site
Written by one of the top biostatisticians
in the field,
Statistics for Epidemiology carves a substantial
niche in a large
market by explaining the key ideas behind
the analysis of
epidemiological data without requiring a
high level of
mathematics and without resorting a 'cookbook'
format. It covers
the basic material for analyzing data that
arise from simple
epidemiological studies, including case-control
and matched
studies. Methodologically, it describes stratification
techniques
for handling confounding and interaction,
and the logistic
regression model. A case-study approach to
the techniques is
used, following a few simple, readily understood
examples through
several method chapters, rather than introducing
a different
example at each stage.
Publication date: March 2004, Hardback, c448pp,
ISBN: 0-340-80752-0,
Reviews of first edition:
'A clearly written and comprehensive account...an
excellent book
in an excellent series.' Mathematics Today
eThis very well-written book has been designed
to complement
the Kendallfs series by presenting therein
the Bayesian point
of view. c The author has skilfully managed
to cover a great
deal of ground in this volume and readers
will find few topics of
interest to be missing.f
Short Book Reviews
Key Features:
Clearly written with a comprehensive coverage
of the theory and
methodology underlying all Bayesian methods
The most up-to-date account
Includes chapters on robustness, computation
and MCMC methods
Exercises supplied at the end of each chapter.
Description:
The Bayesian approach to statistics is now
widely accepted as
theoretically sound and practically viable.
Enormous advances in
Bayesian methodology in recent years have
resulted in a great
expansion of applications of Bayesian statistics
in a wide
variety of fields. This second edition is
a response to the
developments and advances that have taken
place in this area over
the last few years and offers the reader
an up-to-date and
comprehensive overview of Bayesian statistics.
The new edition of Bayesian Inference has
been expanded to
include new chapters on Markov chain Monte
Carlo methods,
discrete data models and non-parametric models.
Existing chapters
have also been thoroughly revised and updated
and there is
greater coverage of computational methods
and of model comparison
and criticism. There is also a new chapter
of case studies,
providing practical illustrations of the
theory presented
throughout the book.
Like the other volumes in the Kendallfs
Library of Statistics,
the first edition of Bayesian Inference provided
a good selection
of exercises at the end of each chapter.
This popular feature is
retained in the new edition, with many new
exercises to deepen
the readerfs understanding.
Readership:
All statisticians and anyone needing to know
more about Bayesian
statistics.
Contents:
Preface
Glossary of Abbreviations
1. The Bayesian method
2. Inference and decisions
3. General principles and theory
4. Subjective probability
5. Non-subjective theories
6. Prior distributions
7. Model comparison
8. Robustness and model criticism
9. Computation
10. Markov Chain Monte Carlo
11. The linear model
12. Discrete data models
13. Nonparametric models
14. Other standard models
15. Short case studies
Bibliography
Author index
Subject index