Books and Media Reviews

Last updated 30 April 2018

Jump to list of books

Review Guidelines

Reviews should be informative and express a view. While most reviews are of books, we welcome suggestions for review of any material (e.g. video or audio, online courses) relevant to statisticians. Please contact the Reviews Editor (see below) with any suggestions.

Readers want to know whether this book (or other material) could be of interest to them, or to a colleague or student. Space for reviews is limited, so every word used must earn its place. A minority of books merit a very full review, about 600–800 words; most reviews are expected to be about 300–400 words; for some books, including those little changed from a previous edition, 150 words will suffice. For Significance magazine, reviews must be no more than 250 words. Please try hard not to include formulae or complex mathematical expressions.

Do not hold back from offering fair and defensible (but not offensive) criticism where it is deserved. If your review exposes a book as outdated, inaccurate or unsatisfactory in other ways, you will earn the gratitude of many. Similarly, when the book has a refreshing perspective, or is particularly useful (even in a few chapters), your enthusiasm will be appreciated. Of course, you must not review material in which you have a pecuniary or similar interest.

Avoid simply quoting from the publisher’s blurb, without comment, or merely listing chapter titles, unless this is the best way to succinctly describe the content. Your review should offer more than can be found by a reader stumbling across the book in a bookshop, or advertised on a Web site. If the authors have offered to make publicly available a list of misprints and corrections, it will be more useful to send minor slips directly to them than to take up space in your review. But, when you find errors that are likely to mislead, then point them out!

Sometimes, two or more books on the same topic can be reviewed together; in general, comparisons of new books with the existing literature can be most helpful. We want reviews published in our journals to read well, to be authoritative, and to be useful to the statistical community. If you refer to other published work, give precise details in the conventional manner, listing such references at the end of your review.

The division between publication in Significance and Series A of the journal is now established: reviews of books aimed at the general public, undergraduate texts and historical surveys now appear in Significance, whereas more technical books, research monographs and postgraduate texts will be reviewed in Series A.

Head your review with the standard information in this order: title, author(s), publication year, edition or format, publisher, length, price and ISBN. End with your own name and your affiliation (or simply town or city where you live), and your e-mail address if you are happy for it to appear in print. Send as plain text or Word document; LaTeX markup may also be helpful if typographic features or non-English characters are relied on.

The Book Reviews Editor and/or the copy editor might edit your review mildly, mainly to put it into in ‘house style’, but also to correct typographical errors etc.; if an Editor wishes to make any alteration of substance, he or she will run it past you first. You will, of course, also receive proofs of the text to check before publication, but the review cannot be published until you also return a signed copy of the copyright transfer agreement (CTA) that will accompany the proofs.

The following items are currently available (listed by year of publication). There is no fee but the reviewer keeps the book. Please contact the Reviews Editor (reviews@rss.org.uk) to request an item.

2018

Borcard, Daniel; Legendre, Pierre & Gillet, François. Numerical Ecology with R. Use R! series. Springer (2018).
Bose, Arup. Patterned Random Matrices. Chapman & Hall (06/2018) ISBN 978-1-138-59146-2
Broemeling, Lyle D. Bayesian Methods for Repeated Measures. Chapman & Hall (04/2018) ISBN 978-1-138-89404-4
Chacón, José E. Multivariate Kernel Smoothing and Its Applications. Chapman & Hall (06/2018) ISBN 978-1-4987-6301-1
Clarke, S. Bernard & Clarke, Jennifer L. Predictive Statistics: Analysis and Inference beyond Models. Cambridge Series in Statistical and Probabilistic Mathematics. CUP (2018) ISBN 978-1-107-02828-9.Hbk
Coffey, Todd Statistics for Biotechnology Process Development. Chapman & Hall (06/2018) ISBN 978-1-4987-2140-0
Cook, Richard J Multistate Models for the Analysis of Life History Data. Chapman & Hall (05/2018) ISBN 978-1-4987-1560-7
Crane, Harry Probabilistic Foundations of Statistical Network Analysis. Chapman & Hall (04/2018) ISBN 978-1-138-63015-4
Desjardins, Christopher D. Handbook of Educational Measurement and Psychometrics Using R. Chapman & Hall (05/2018) ISBN 978-1-4987-7013-2
Dickhaus, Thorsten. Theory of Nonparametric Tests. Springer (2018).
Dobson, Annette J. An Introduction to Generalized Linear Models, Fourth Edition. Chapman & Hall (04/2018) ISBN 978-1-138-74151-5
Farrington, Paddy Self-Controlled Case Series Studies: A Modelling Guide with R. Chapman & Hall (05/2018) ISBN 978-1-4987-8159-6
Feng, Runhuan An Introduction to Computational Risk Management of Equity-Linked Insurance. Chapman & Hall (06/2018) ISBN 978-1-4987-4216-0
Fu, Wenjiang A Practical Guide to Age-Period-Cohort Analysis: The Identification Problem and Beyond. Chapman & Hall (05/2018) ISBN 978-1-4665-9265-0
Gil, Eduardo; Gil, Juan; Gil, María Ángeles & Gil, Eva. The Mathematics of the Uncertain. Studies in Systems, Decision and Control, Vol. 142. Springer (2018).
Hofmann, Bernd; Leitao, Antonio & Zubelli, Jorge Passamani. New Trends in Parameter Identification for Mathematical Models. Trends in Mathematics series. Springer (2018).
Keen, Kevin J. Graphics for Statistics and Data Analysis with R, Second Edition. Chapman & Hall (05/2018) ISBN 978-1-4987-7983-8
Lawson, Andrew B. Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Third Edition. Chapman & Hall (05/2018) ISBN 978-1-138-57542-4
Li, Bing Sufficient Dimension Reduction: Methods and Applications with R. Chapman & Hall (05/2018) ISBN 978-1-4987-0447-2
Link, Daniel. Data Analytics in Professional Soccer. Springer (2018)
Lo, Ambrose Derivative Pricing: A Problem-Based Primer. Chapman & Hall (05/2018) ISBN 978-1-138-03335-1
Mias, George. Mathematica for Bioinformatics. Springer (2018)
Mola, Francesco; Conversano, Claudio & Vichi, Maurizio. Classification, (Big) Data Analysis and Statistical Learning. Studies in Classification. Springer (2018)
Raghunathan, Trivellore Multiple Imputation in Practice: With Examples Using IVEware. Chapman & Hall (06/2018) ISBN 978-1-4987-7016-3
Resendis-Antonio, Osbaldo & Olivares-Quiroz, Luis (Eds) Quantitative Models for Microscopic to Macroscopic Biological Macromolecules and Tissues. Springer (2018)
Rose, Sherri ; van der Laan, Mark J. Targeted Learning in Data Science. Springer Series in Statistics. Springer (2018)
Shoukri, Mohamed M. Analysis of Correlated Data with SAS and R, Fourth Edition. Chapman & Hall (04/2018) ISBN 978-1-138-19745-9
Skansi, Sandro. Introduction to Deep Learning. Undergraduate Topics in Computer Science. Springer (2018)
Vaz, A. Ismael F.; Pinto, Alberto Adrego; Oliveira, José Fernando & Almeida, Joao. Operational Research. Springer Proceedings in Mathematics & Statistics, Vol. 223. Springer (2018)
Vexler, Albert Empirical Likelihood Methods in Biomedicine and Health. Chapman & Hall (06/2018) ISBN 978-1-4665-5503-7
von Rosen, Dietrich & Tez, Müjgan. Trends and Perspectives in Linear Statistical Inference. Springer (2018)
Wu, Colin O. Nonparametric Models for Longitudinal Data: With Implementation in R. Chapman & Hall (06/2018) ISBN 978-1-4665-1600-7
Wu, Jianrong Statistical Methods for Survival Trial Design: With Applications to Cancer Clinical Trials Using R. Chapman & Hall (06/2018) ISBN 978-1-138-03322-1
Xiong, Momiao Big Data in Omics and Imaging: Integrated Analysis and Causal Inference. Chapman & Hall (06/2018) ISBN 978-0-8153-8710-7
Zwinderman, Aeilko H. & Cleophas, Ton J. Regression Analysis in Medical Research. Springer (2018)
Russell, J.A. Statistics in Music Education Research. OUP (2018) ppb
[A range of statistical methods described with specific examples from this domain]
Stone, J.V. Principles of Neural Information Theory: Computational Neuroscience and Metabolic Efficiency. Sebtel Press (2018)
[Contains much mathematical analysis, relating information theory to neural computation]
Cate F.H. and Dempsey J.X. (eds) Bulk Collection: Systematic Government Access to Private-Sector Data. OUP (2018) hbk
[A survey of surveillance practices around the world]
Adams, J.A. A Mathematical Nature Walk. Princeton University Press (2011) pbk
[I think I misread the year as 2017 & ordered it, but it contains lots of examples of estimating real-world values and is suggested as an ideas book for "teachers of science and mathematics", though probably at A-level at least.]
Collazo, Rodrigo A., Görgen, C. & Smith, J. Q. Chain Event Graphs. CRC Press (2018) hbk
Efromovich, S. Missing and Modified Data in Nonparametric Estimation: With R Examples. CRC Press (2018) hbk
Gould, W. The Mata Book: A book for serious programmers and those who want to be. Stata Press. (2018) pbk
Harville, David A. Linear Models and the Relevant Distributions and Matrix Algebra. Chapman & Hall/CRC Texts in Statistical Science (2018) hbk/ebook
Micheas, Athanasios C. Theory of Stochastic Objects: Probability, Stochastic Processes and Inference. CRC Press (2018) hbk/ebook
Pardo, S. & Pardo M. Statistical Methods for Field and Laboratory Studies in Behavioral Ecology. Chapman & Hall/CRC Applied Environmental Statistics (2018) hbk
Vexler, A. & Hutson, A. Statistics in the Health Sciences: Theory, Applications, and Computing. CRC Press (2018) hbk/ebook
Anderson, D.F., Seppäläinen, T. and Valkó, B. Introduction to Probability. CUP. (2018) hbk
Murtagh, F. Data Science Foundations: Geometry and Topology of Complex Hierarchical Systems and Big Data Analysis. CRC Press. (2018) hbk

2017

Allen, R. Statistics and Experimental Design for Psychologists: A Model Comparison Approach. World Scientific. (2017) pbk
Barlow, M. Random walks and heat kernels on graphs. CUP. (2017) pbk
Beckerman, A. P., Childs, D. Z. and Petchey, O. L. Getting Started with R: An Introduction for Biologists. (2nd ed.) OUP. (2017) pbk
Bruce, P. and Bruce, A. Practical Statistics for Data Scientists. O’Reilly. (2017) pdf
Golbeck, A. Equivalence: Elizabeth L. Scott at Berkeley. CRC Press. (2017) pbk
Greenacre , M.J. Correspondence Analysis in Practice. (3rd ed.) CRC Press. (2017) hbk
Kedem, B., De Oliveira, V. and Sverchkov, M. Statistical Data Fusion. World Scientific Publishing. (2017) hbk
Lawson, J. and Erjavec, J. Basic Experimental Strategies and Data Analysis for Science and Engineering. CRC Press. (2017) hbk
Magallenes Reyes, J.M. Introduction to Data Science for Social and Policy Research: Collecting and Organizing Data with R and Python. CUP. (2017) pbk
Mai, J-F. and Scherer, M. (Eds) Simulating copulas: Stochastic models, Sampling Algorithms and Applications. (2ed) (Series in quantitative finance 6) World Scientific. (2017) hbk
Manly, B.F.J. and Navarro Alberto, J.A. Multivariate Statistical Methods: A primer (4th ed). CRC Press. (2017) pbk
Matloff, N. Statistical Regression and Classificiation: From linear models to machine learning. (Texts in Statistical Science) CRC Press. (2017) hbk
Menshikov, M., Popov, S. and Wade, A. Non-homogenous random walks: Lyapunov function methods for near critical stochastic systems. CUP. (2017) hbk
O’Neill, R., Ralph, J. and Smith, P. Inflation: History and Measurement. Palgrave Macmillan. (2017) pdf
Renwick, C. Bread for All: The Origins of the Welfare State. Allen Lane. (2017) hbk
Smeeton, N.C. Dental Statistics Made Easy (3rd ed). CRC Press. (2017) pbk
Wilcox, R. Modern Statistics for the Social and Behavioral Sciences [using R] (2nd ed). CRC Press. (2017) hbk

2016

Aston, P., Mulholland, A. and Tant, K. (eds.) UK Success Stories in Industrial Mathematics. Springer. (2016) hbk
Giné, E. and Nickl, R. Mathematical Foundations of Infinite Dimensional Statistical Models. (Cambridge Series in Statistical and Probabilistic Mathematics) CUP. (2016) hbk
Martin, R. and Liu C. Inferential Models: Reasoning with uncertainty. Monograph on statistics 147. CRC Press. (2016) hbk
McCarroll, D. Simple Statistical Tests for Geography. CRC Press. (2016) pbk
Schweder, T. and Hjort, N.L. Confidence, Likelihood, Probability: Statistical inference with confidence distributions (Cambridge Series in Statistical and Probabilistic Mathematics). CUP. (2016) hbk
Yang, H., Zhang, J., Yu, B. and Zhao, W. Statistical Methods for Immunogenicity Assessment. CRC Press. (2016) hbk