George Michailides

English

Research Papers in Economics

JSS Journal of Statistical Software
July 2004, Volume 11, Book Review 6. http://www.jstatsoft.org/
Reviewer: George Michailidis
University of Michigan
Computational Statistics Handbook with MATLAB
Wendy L. Martinez and Angel R. Martinez
Chapman & Hall/CRC, Boca Raton, Florida, 2002.
ISBN 1-58488-229-8. xvii + 591 pp. $89.95.
The stated goals of this book by its authors are (page xv on the Preface):
• To make computational statistics techniques available to a wide range of users, including
engineers and scientists, and
• To promote the use of MATLAB by statisticians and other data analysts.
Before we examine how successful their undertaking is, we provide a brief summary of the
contents of the book. Chapter 1 gives an overview of the topics covered in the book together
with a brief description of MATLAB code and availability of MATLAB resources on the Web.
Chapter 2 provides a basic discussion of probability concepts, whereas Chapter 3 discusses
sample statistics (mean, variance, moments, etc.) and parameter estimation methods (maxi-
mum likelihood, method of moments). Chapter 4 is about generating discrete and continuous
random variables with an emphasis on the inversion and rejection methods. Chapter 5 dis-
cusses exploratory data analysis concepts with a strong emphasis on graphical methods. A
fairly detailed description of how to take advantage of the advanced graphics capabilities of
MATLAB to make nice and informative plots is provided. Chapter 6 introduces the reader to
Monte Carlo and boostrap methods and their use for hypothesis testing and construction of
conﬁdence intervals, while Chapter 7 covers cross-validation, jackkniﬁng and its combinations
with the bootstrap. Linear regression is introduced as a way to motivate the concept of cross-
validation. Chapter 8 discusses density estimation methodology (histograms, kernel-based
techniques, ﬁnite mixtures), where an application of the EM algorithm appears. Selected
classiﬁcation and clustering techniques are presented in Chapter 9, which also contains a
nice discussion of ROC curves. Chapter 10 covers non-parametric regression, including lo-
cal smoothing and kernel methods, as well as regression trees. Chapter 11 introduces the
readers to Markov Chain Monte Carlo basics and discusses the Metropolis-Hastings and the
Gibbs sampler algorithms, together with convergence monitoring methods. Finally, Chapter
12 focuses on modeling, visualizing and simulating spatial point processes. There are also
four appendices that provide a quick introduction to MATLAB (Appendix A), a summary of2 Computational Statistics Handbook with MATLAB
the notation used in the book (Appendix B), a discussion of several projection pursuit in-
dices (Appendix C) and the source code of some of the more complicated MATLAB functions
discussed in the book (Appendix D).
Almost all the concepts discussed in the book are illustrated with MATLAB code, which
although not always particularly eﬃcient, is nonetheless well documented and easy to under-
stand. Every chapter is complemented with several exercises of varying degree of diﬃculty
and with an informative set of ’Further Reading’ notes.
The book is fairly successful in achieving the authors’ second goal, but deﬁnitely falls short
with respect to their ﬁrst goal. MATLAB has become the language of choice for engineers and
applied mathematicians and is also quite popular within some statistics areas (e.g. wavelet
analysis and time series analysis in general). By providing well documented, although not
optimized, code for many widely used statistical techniques, the authors would help expand
the MATLAB community within statistics.
On the other hand, there are several important topics not covered at all in the book, such as
generalized linear models, mixed and random eﬀects models, time series analysis, and survival
analysis, that can be found in a book with similar scope (see Venables and Ripley 2002). There
are also some important omissions from the topics covered, such as linear and quadratic
discriminant techniques and the k-means clustering algorithm in Chapter 9, spline methods
in Chapter 10, krieging in Chapter 12, and a more in depth discussion of linear regression,
just to name a few. Furthermore, the presentation of the material is rather superﬁcial and
a discussion of the strengths and weaknesses of the various techniques is lacking. On the
positive side, the material is well motivated and its exposition is clear and lucid.
Nevertheless, I think that this book is perfectly appropriate as a textbook for an introductory
course on computational statistics. It covers many useful topics, which in combination with
the well-documented code make the underlying concepts easy to grasp by the students. It
also brings to the forefront the powerful graphics capabilities of MATLAB.
Overall, this is a very nice book to be used in an undergraduate or Masters level computational
statistics course. It would also prove useful to researchers in other ﬁelds that want to learn
and implement quickly some advanced statistical techniques. However, it falls short as a
computational statistics handbook, both in terms of depth and to some extent of breadth of
coverage of topics.
References
Venables WN, Ripley BD (2002). Modern Applied Statistics with S. Fourth Edition. Springer.
ISBN 0-387-95457-0, URL http://www.stats.ox.ac.uk/pub/MASS4/.Journal of Statistical Software – Book Reviews 3
Reviewer:
George Michailidis
University of Michigan
Department of Statistics
Ann Arbor, MI 48109, USA
E-mail: gmichail@umich.edu
Journal of Statistical Software Published: 2004-07-04
July 2004, Volume 11, Book Review 6.
http://www.jstatsoft.org/

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