Books and Media Reviews
The following items are currently available (listed by year of publication). There is no fee but the reviewer keeps the book, except when publishers make an ebook available for limited time. Please contact the reviews editor (kkumar@bond.edu.au) to request an item.
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Books available
Adams, Christopher P.; Learning Microeconometrics with R; Chapman and Hall/CRC Press
Akalin, Altuna; Computational Genomics with R; Chapman and Hall/CRC Press
Alemayehu, Demissie; Interface between Regulation and Statistics in Drug Development; Chapman and Hall/CRC Press
Armstrong, David A.; Analyzing Spatial Models of Choice and Judgment; Chapman and Hall/CRC Press
Bailer, A.; Statistical Programming in SAS; Chapman and Hall/CRC Press
Banks, David L.; Handbook of Forensic Statistics; Chapman and Hall/CRC Press
Bose, Arup; Random Circulant Matrices; Chapman and Hall/CRC Press
Brémaud, Pierre; Probability Theory and Stochastic Processes; Springer
Chang, Mark; Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare; Chapman and Hall/CRC Press
Chen, Xinguang, and Chen, Ding-Geng; Statistical Methods for Global Health and Epidemiology; Springer
Chow, Shein-Chung; Innovative Methods for Rare Disease Drug Development; Chapman and Hall/CRC Press
Cole, Diana; Parameter Redundancy and Identifiability; Chapman and Hall/CRC Press
Curry, Edward; Introduction to Bioinformatics with R: A Practical Guide for Biologists; Chapman and Hall/CRC Press
Edge, M. D.; Statistical Thinking from Scratch: a Primer for Scientists; Oxford University Press
Faraway, Julian J.; Linear Models with Python; Chapman and Hall/CRC Press
Forsberg, Ole J.; Understanding Elections through Statistics: Polling, Prediction, and Testing; Chapman and Hall/CRC Press
Foster, Ian; Big Data and Social Science: Data Science Methods and Tools for Research and Practice; Chapman and Hall/CRC Press
Gentle, James; Statistical Analysis of Financial Data: with Examples In R; Chapman and Hall/CRC Press
Gerbing, David; R Visualizations: Derive Meaning from Data; Chapman and Hall/CRC Press
Gerds, Thomas A.; Medical Risk Prediction Models: With Ties to Machine Learning; Chapman and Hall/CRC Press
Gillard, Jonathan; A First Course in Statistical Inference; Springer
Goad, Carla L.; SAS Programming for Elementary Statistics: Getting Started; Chapman and Hall/CRC Press
Golden, Richard; Statistical Machine Learning: a Unified Framework; Chapman and Hall/CRC Press
Greselin, Francesca, Vichi, Maurizio, Deldossi, Laura, and Bagnato, Luca; Statistical Learning of Complex Data; Springer
Haining, Robert P.; Modelling Spatial and Spatial-temporal Data: a Bayesian Approach; Chapman and Hall/CRC Press
Hand, David J.; Dark Data: Why What You Don’t Know Matters; Princeton University Press
Harrison, Ewen; R for Health Data Science; Chapman and Hall/CRC Press
Hoang, Lê Nguyên; The Equation of Knowledge: from Bayes' Rule to a Unified Philosophy of Science; CRC Press
Ismay, Chester; Statistical Inference via Data Science; Chapman and Hall/CRC Press
Kirk, Andy; Data Visualisation: A Handbook for Data Driven Design, 2nd Edition; Sage
Kitagawa, Genshiro; Introduction to Time Series Modeling with Applications in R; Chapman and Hall/CRC Press
Klein, John P.; Handbook of Survival Analysis; Chapman and Hall/CRC Press
Lawrence, Andy; Probability in Physics; Springer
Lawson, John; An Introduction to Acceptance Sampling and SPC with R; Chapman and Hall/CRC Press
Leemis, Larry; Mathematical Statistics; Taylor and Francis
Leemis, Larry; Probability, 2nd Edition; Taylor and Francis
Lesaffre, Emmanuel; Bayesian Methods in Pharmaceutical Research; Chapman and Hall/CRC Press
Lyubchich, Vyacheslav; Evaluating Climate Change Impacts; Chapman and Hall/CRC Press
Maltenfort, Mitchell; Statistical Reasoning for Surgeons; Chapman and Hall/CRC Press
McElreath, Richard; Statistical Rethinking: a Bayesian Course with Examples in R and STAN; Chapman and Hall/CRC Press
McElroy, Tucker S.; Time Series: a First Course with Bootstrap Starter; Chapman and Hall/CRC Press
Mehmetoglu, Mehmet; Structural Equation Modelling with Partial Least Squares Using Stata and R; Chapman and Hall/CRC Press
Michel, René, von Martens, Tobias, and Schnakenburg, Igor; Targeting Uplift: an Introduction to Net Scores; Springer
Myers, Chelsea; Project-Based R Companion to Introductory Statistics; Chapman and Hall/CRC Press
Oakland, John S., Oakland, Robert J. and Turner, Michael A.; Total Quality Management and Operational Excellence: Text with Cases, 5th edn; Routledge
Paradis, Emmanuel; Population Genomics with R; Chapman and Hall/CRC Press
Peng, Shige; Nonlinear Expectations and Stochastic Calculus under Uncertainty; Springer
Peng, Yingwei; Cure Models: Methods, Applications, and Implementation; Chapman and Hall/CRC Press
Prügel-Bennett, Adam; The Probability Companion for Engineering and Computer Science; Cambridge University Press
Quirk, Thomas J.; Excel 2019 for Engineering Statistics; Springer
Rabbee, Nusrat; Biomarker Analysis in Clinical Trials with R;
Rahman, Azizur; Statistics for Data Science and Policy Analysis; Springer
Razzaghi, Mehdi; Statistical Models in Toxicology; Chapman and Hall/CRC Press
Rigdon, Steven E.; Monitoring the Health of Populations by Tracking Disease Outbreaks: Saving Humanity from the Next Plague; Chapman and Hall/CRC Press
Rios Insua, David; Security Risk Models for Cyber Insurance; Chapman and Hall/CRC Press
Roback, Paul; Beyond Multiple Linear Regression: Applied Generalized Linear Models And Multilevel Models in R; Chapman and Hall/CRC Press
Roncalli, Thierry; Handbook of Financial Risk Management; Chapman and Hall/CRC Press
Severini, Thomas A.; Analytic Methods in Sports: Using Mathematics and Statistics to Understand Data from Baseball, Football, Basketball, and Other Sports; Chapman and Hall/CRC Press
Sievert, Carson; Interactive Web-Based Data Visualization with R, plotly, and shiny; Chapman and Hall/CRC Press
Sijtsma, Klaas; Measurement Models for Psychological Attributes; Chapman and Hall/CRC Press
Tartakovsky, Alexander; Sequential Change Detection and Hypothesis Testing; Chapman and Hall/CRC Press
Urdinez, Francisco; R for Political Data Science: A Practical Guide; Chapman and Hall/CRC Press
Walden, Andrew T. and Percival, Donald B.; Spectral Analysis for Univariate Time Series; Cambridge University Press
Washington, Simon; Statistical and Econometric Methods for Transportation Data Analysis; Chapman and Hall/CRC Press
Whitmore, Nathan; R for Conservation and Development Projects: A Primer for Practitioners; Chapman and Hall/CRC Press
Wu, Hulin; Statistics and Machine Learning Methods for EHR Data: From Data Extraction to Data Analytics; Chapman and Hall/CRC Press
Xie, Yihui; R Markdown Cookbook; Chapman and Hall/CRC Press
Zhang, Hongmei; Analyzing High-Dimensional Gene Expression and DNA Methylation Data with R; Chapman and Hall/CRC Press
Zuccolotto, Paola; Basketball Data Science: with Applications in R; Chapman and Hall/CRC Press
Reviews guidelines
Reviews should be informative and express a view. While most reviews are of books, we welcome suggestions for review of any material (eg video or audio, online courses) relevant to statisticians. Please contact the reviews editor 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 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 website. 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.
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Last updated 25 September 2020.