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Journal of the Royal Statistical Society Series A (General) Statistics Topics S.Mehta, 2014 CreateSpace Independent Publishing Platform 160 pp., £4.50 ISBN...
Statistics Topics S.Mehta, 2014 CreateSpace Independent Publishing Platform 160 pp., £4.50 ISBN 9781499273533
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Cilt:
178
Dil:
english
Dergi:
Journal of the Royal Statistical Society: Series A (Statistics in Society)
DOI:
10.1111/j.1467985x.2014.12096_2.x
Date:
January, 2015
Dosya:
PDF, 667 KB
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Book Reviews Bayesian Data Analysis, 3rd edn A. Gelman, J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari and D. B. Rubin, 2013 Boca Raton, Chapman and Hall–CRC 676 pp., £44.99 ISBN 1439840954 The authors suggest three audiences: a graduate text and a ﬁrstprinciples general introduction to Bayesian inference as well as a handbook of Bayesian methods in applied statistics for general users. The book certainly ﬁts the ﬁrst very well, although it would require that the instructor supplement endofchapter problems (and intext exercises) with further details. As either a general introduction or a text for a general user it should be noted that the book requires familiarity with probability and statistical modelling. It does not have the detailed development of theory in a way that is presented by a text such as Robert (2001); nor does it have the detailed development of algorithms such as Robert and Casella (1999). What it does do though is motivates Bayesian methods through applications. At the same time, it is deﬁnitely not an applied statistics cookbook. However brief or informal the mathematical development may seem to some readers, the theoretical underpinnings of models and ﬁtting methods are lucidly outlined and are readily followed. Indeed, there are chapters such as Chapter 8 ‘Modeling accounting for data collection’ which outlines key topics such as survey methods, randomized trials, causal inference, censoring and truncation and Chapter 18 ‘Models for missing data’. It seems fairly rare to ﬁnd these topics in many nonspecialist books. The third edition sees an addition of two new authors and new material in terms of the models ﬁtted such as Chapter 20 (‘Basis function models’), Chapter 21 (‘Gaussian process models’) and Chapter 23 (‘Dirichlet process models’) as well as the ﬁtting methods. For the material presented on ﬁtting algorithms, perhaps Hamiltonian Monte Carlo sampling (section 12.4) and STAN software have pride of place; nevertheless this is not a software handbook and most material; can be read independently of thoughts about particular software implementation. Indeed, mention is also given to variational inference, expectation propagation, approximate Bayesian computation and a very brief mention of particle ﬁltering (page 300; not indexed that I could see) as well as material on posterior approximations. Appendix C, as in earlier editions, provides a very brief overview of computer implementation for Gibbs, Metropolis and Hamiltonian Monte Carlo sampling by using both R coding to see the workings as well as STAN as an introduction to this software. 301 Where this book excels is that it contains a wealth of practical experience, set in the context of a coherently presented text on modern Bayesian modelling. Prior choice is explored in many applications such as section 5.7 which examines priors for hierarchical variance parameters. Chapter 6 covers ‘Model checking’ and Chapter 7 ‘Evaluating, comparing and expanding models’. I guess that these chapters set out the authors’ opinions (for example outofsample predictions are rated highly) and that other authors might view things differently (placing more emphasis on regularization methods) but they deserve careful attention by drawing attention to these often neglected topics. Overall, this is an excellent book. It would be a valuable addition to any institutional library. The third edition offers much that is not contained in the second and it is worth upgrading. In terms of personal purchase the decision rests entirely on what the reader is looking for in a Bayesian textbook. This book is quite clear in what it wishes to be, and it fulﬁls that aim admirably. References Robert, C. P. (2001) The Bayesian Choice: from Decisiontheoretic Foundations to Computational Implementation. New York: Springer. Robert, C. P. and Casella, G. (1999) Monte Carlo Statistical Methods. New York: Springer. Paul Hewson Plymouth University Email: paul.hewson@plymouth.ac.uk Statistics Topics S. Mehta, 2014 CreateSpace Independent Publishing Platform 160 pp., £4.50 ISBN 9781499273533 The author of the independently published Statistical Topics has set himself a worthy but hard task in producing a book to cater for many audiences, especially those in developing countries. With this goal in mind the electronic version of this book has a minimal price (£0.77) and half the proﬁts are stated to go to charity. The book is written from the perspective of an American author with experience of ﬁnancial risk modelling, which informs the book’s choice of some examples—others are topical like the Malaysian aircraft search from May 2014. The language is grounded in American concepts (for example an ‘Arnold Palmer’ is a cocktail!) and illustrated with pseudorealworld examples (e.g. the classic Monty 302 Book Reviews Hall door problem). However, the book does assume a certain level of understanding, as many terms are not explained or are referred to but are not explained till much later in the text. Athough the tone of the book is conversational and approachable in style, the structure of the book is left wanting; for example all formulae in the text are displaced to the appendix (Kindle also has problems displaying equations). There are systematic spelling and grammatical errors, typographical mistakes and errors in examples (e.g. coin ﬂip outcomes) which unfortunately distract from the content and might derail students from working through the material and prevent them from fully comprehending the concepts. Examples of structural issues are referring to conﬁdence intervals but only explaining them 27 pages later, or introducing multiple logistic regression before simple regression. The addition of a glossary would also have greatly helped. Although the book has a worthy aim, if it were to have an editorial process the next edition would hopefully be more readable and have more to recommend it over existing texts (e.g. the free online Statistics at Square One). Mark Pilling University of Manchester Email: mark.pilling@manchester.ac.uk Understanding Advanced Statistical Methods P. Westfall and K. S. S. Henning, 2013 Boca Raton, Chapman and Hall–CRC 570 pp., £44.99 ISBN 9781466512108 Most statistics textbooks are either too technical and fail to engage students, or they are too applied, in which case they are merely cookbooks providing sets of formulae. This book brings new light on standard topics and presents statistics as the ‘language of science’, in which the data give a clearer picture of what nature is and how it works. It takes a modern approach, moving away from the notion of population. The population approach ignores the measurement and design mechanisms and does not allow generalizability. Instead, the book focuses on understanding and describing the processes that produce the data and on identifying suitable models for them. The book owes its success, at least partially, to its simple informal language and engaging style. It provides intuitive explanations for concepts and representations that both students and researchers struggle with. Mathematical notation, formulae and derivations are given. Still, these are kept to an essential minimum, with the exception of the discussion on likelihood ratio tests, a topic which, by its nature, requires more advanced mathematical derivations. The contents of this book would attract the curious student or researcher, interested in understanding the reasons why certain statistical methods work, rather than just learning to apply those methods. Mathematics students and students from other disciplines can equally beneﬁt from reading this book to appreciate the fundamental assumptions and the meaning behind the concepts. In my view, the reader should have some prior knowledge, or at least some experience in dealing with similar problems on the subjects covered, before attempting to appreciate the book’s intention. In any case, it does not set out to be exhaustive; there are things left unexplored, for which the authors give references or urge the reader to research on. The structure of the book is similar to that of most statistics textbooks. However, the approach and the presentation of concepts are unique in their explanatory power. Probability, random variables and probability distributions, as well as conditional, marginal, joint distributions and independence, are introduced in the ﬁrst six chapters. Chapter 7 is unusual in that it insists on the distinction between populations and processes. The rest of the chapters are common in most statistics textbooks, covering topics such as the law of large numbers, the central limit theorem, maximum likelihood estimation and properties of estimators and regression modelling, as well as Bayesian inference. The authors suggest that one should choose what is appropriate given the problem, rather than dogmatically decide between frequentist and Bayesian methods. Still, their discussion suggests a preference towards the latter. Methods that are usually presented as afterthoughts and in more advanced textbooks, such as the bootstrap plugin principle and randomization tests, are employed early on to facilitate explanation of concepts. At the end of each chapter, a summary of the notions covered is given, helping the reader to consolidate ideas. A set of exercises for each chapter, which are of similar level and style with the examples, contributes towards deeper understanding. Demonstrations and examples use Microsoft Excel, aiming to convince the reader of the simplicity of even the most advanced statistical methods. Overall, the book is very well written and well organized, even though its chapters are not stand alone; they are part of a book that is intended to be read as a whole. It includes some very insightful interpretations, as well as a set of ‘ugly rules’ that anyone with any degree of interest in statistics should be aware of. Irene Kaimi Plymouth University Email: irene.kaimi@plymouth.ac.uk