2002. X, 190 p. 112 illus. Softcover
0-387-95414-7
What do juggling, old bones, criminal careers and human growth
patterns have in common? They all give rise to functional data,
that come in the form of curves or functions rather than the
numbers, or vectors of numbers, that are considered in
conventional statistics. The authors' highly acclaimed book
Functional Data Analysis (1997) presented a thematic approach to
the statistical analysis of such data. By contrast, the present
book introduces and explores the ideas of functional data
analysis by the consideration of a number of case studies, many
of them presented for the first time. The two books are
complementary but neither is a prerequisite for the other. The
case studies are accessible to research workers in a wide range
of disciplines. Every reader, whether experienced researcher or
graduate student, should gain not only a specific understanding
of the methods of functional data analysis, but more importantly
a general insight into the underlying patterns of thought. Some
of the studies demand the development of novel aspects of the
methodology of functional data analysis, but technical details
aimed at the specialist statistician are confined to sections
which the more general reader can safely omit. There is an
associated web site with MATLABr and S?PLUSr implementations of
the methods discussed, together with all the data sets that are
not proprietary. Jim Ramsay is Professor of Psychology at McGill
University, and is an international authority on many aspects of
multivariate analysis. He was elected President of the
Statistical Society of Canada for the term 2002-3 and is a holder
of the Society's Gold Medal for his work in functional data
analysis. His statistical work draws on his collaborations with
researchers in speech articulation, biomechanics,
Contents: Introduction.- Life Course Data in Criminology.- The
Nondurable Goods Index.- Bone Shapes from a Paleopathology Study.-
Modeling Reaction Time Distributions.- Zooming in on Human Growth.-
Time Warping Handwriting and Weather Records.- How do Bone Shapes
Indicate Arthritis?- Functional Models for Test Items.-
Predicting Lip Acceleration from Electromyography.- Variable
Seasonal Trend in the Goods Index.- The Dynamics of Handwriting
Printed Characters.- A Differential Equation for Juggling.
Series: Springer Series in Statistics.
2002. XIV, 434 pp. 126 figs., with CD-ROM. Hardcover
0-387-95351-5
Some of the key mathematical results are stated without proof in
order to make the underlying theory accessible to a wider
audience. The book assumes a knowledge only of basic calculus,
matrix algebra, and elementary statistics. The emphasis is on
methods and the analysis of data sets. The logic and tools of
model-building for stationary and nonstationary time series are
developed in detail and numerous exercises, many of which make
use of the included computer package, provide the reader with
ample opportunity to develop skills in this area.
The core of the book covers stationary processes, ARMA and ARIMA
processes, multivariate time series and state-space models, with
an optional chapter on spectral analysis. Additional topics
include harmonic regression, the Burg and Hannan-Rissanen
algorithms, unit roots, regression with ARMA errors, structural
models, the EM algorithm, generalized state-space models with
applications to time series of count data, exponential smoothing,
the Holt-Winters and ARAR forecasting algorithms, transfer
function models and intervention analysis. Brief introductions
are also given to cointegration and to nonlinear, continuous-time
and long-memory models.
The time series package included in the back of the book is a
slightly modified version of the package ITSM, published
separately as ITSM for Windows, by Springer-Verlag, 1994. It does
not handle such large data sets as ITSM for Windows, but like the
latter, runs on IBM-PC compatible computers under either DOS or
Windows (version 3.1 or later). The programs are all menu-driven
so that the reader can immediately apply the techniques in the
book to time series data, with a minimal investment of time in
the computational and algorithmic aspects of the analysis.
Keywords: Time series, Forecasting, ITSM, ITSM2000
Contents: Introduction.- Stationary Processes.- ARMA Models.-
Spectral Analysis.- Modelling and Forecasting with ARMA Processes.-
Nonstationary and Seasonal Time Series Models.- Multivariate Time
Series.- State-Space Models.- Forecasting Techniques.- Further
Topics.
Series: Springer Texts in Statistics.
2002. XVI, 332 pp. 90 figs., 51 tabs. Hardcover
4-431-70338-1
Diversity is characteristic of the information age and also of
statistics. To date, the social sciences have contributed greatly
to the development of handling data under the rubric of
measurement, while the statistical sciences have made phenomenal
advances in theory and algorithms. Measurement and Multivariate
Analysis promotes an effective interplay between those two realms
of research-diversity with unity. The union and the intersection
of those two areas of interest are reflected in the papers in
this book, drawn from an international conference in Banff,
Canada, with participants from 15 countries. In five major
categories - scaling, structural analysis, statistical inference,
algorithms, and data analysis - readers will find a rich variety
of topics of current interest in the extended statistical
community.
Keywords: measurement and multivariate analysis conference
Contents: Greetings.- Keynote Papers (John Gower, Chikio Hayashi).-
Introduction (S. Nishisato).- Scaling.- Structural Analysis.-
Statistical Inference.- Algorithms.- Data Analysis.
August 2002 | Hardback | 728 pages 57 tables 67 figures | ISBN:
0-521-79018-2
August 2002 | Paperback | 728 pages 57 tables 67 figures | ISBN:
0-521-79367-X
The first textbook to teach introductory econometrics to finance
majors. The text is data- and problem-driven, giving students the
skills to estimate and interpret models, whilst having an
intuitive grasp of the underlying theoretical concepts. The
approach of Dr Brooks, based on the successful course he teaches
at the ISMA Centre, one of Europe's leading finance schools,
ensures that the text focuses squarely on the needs of finance
students, including advice on planning and executing a project in
empirical finance. The book assumes no prior knowledge of
econometrics, and covers important modern topics such as time-series
forecasting, volatility modelling, switching models and
simulation methods. It includes detailed examples and case
studies from the finance literature. Sample instructions and
output from two popular and widely available computer packages (EViews
and WinRATS) are presented as an integral part of the text.
Extensive web-based supporting materials are available free of
charge.
Contents
1. Introduction; 2. Econometric packages for modelling financial
data; 3. A brief overview of the classical linear regression
model; 4. Further issues with the classical linear regression
model; 5. Univariate time series modelling and forecasting; 6.
Multivariate modelling; 7. Modelling long-run relationships in
finance; 8. Modelling volatility and correlation; 9. Modelling
regime shifts; 10. Simulation methods; 11. Conducting empirical
research in finance; 12. Conclusions: recent and future
developments in the modelling of financial time series;
References; Appendix. Review of matrix algebra, calculus, and
probability theory; Statistical tables.