This book will make accessible to a wider audience the new and important area of nonparameteric likelihood and hypothesis testing. It is written by a pioneer in this area. The ideas in this book are very sophisticated but one has no difficulty following the arguments because the writing is crystal clear; the author must have put in a lot of thought into the organization and presentation of the ideas. The book lucidly discusses the statistical theory and -- perhaps more importantly for the practitioner -- computational details and practical aspects of putting the ideas to work with real data. I expect that this book will have a major impact on the way hypothesis testing is done in econometrics and other areas of applied statistics, where one is very often unsure about what the correct model specification is and cannot rely on certain large sample approximations. A terrific book. I would recommend it very highly to practitioners in areas where hypothesis testing is too important to be left to less correct approaches (I am thinking of areas like biostatistics, quality control, econometrics, but I expect there are many such areas).

Prof. Barlow has distilled his knowledge and 25+ years of teaching experience into 190 pages. The text presents the theory very concisely without sacrificing rigor. All the terminology is carefully defined and the meanings are made clear through examples. The examples appear to be drawn from what has been successful in the classroom, and are much more useful than the examples given in early works in this field. The book is divided into three parts, plus a 6 page Introduction to the history and background of the subject. The first part covers Failure Data Analysis. This part defines the terms used later, and develops the mathematics needed in a very intuitive way. The second part covers the Economics of Maintenance Decisions in eleven pages! The third part covers System Reliability. Individual chapters cover Network Reliability, System Failure Analysis, Availability and Maintainability, Influence Diagrams, and Making Decisions Using Influence Diagrams. The book gives very practical, useful information, reflecting its engineering origins. It is the kind of book you will go back to often.

This is the best-written software user's manual I have ever seen

Philosophers of Science attempt to construct a normative methodology for scientific inference. As a practicing scientist who follows this field, my impression is that the majority of the published work in this area falls into one of three categories: Critiquing existing methodologies, improving on existing methodologies, and less frequently, proposing a new methodology that is felt to have solved the philosophical problems of the previous methods. Dr. Mayo's book falls into the third category.

In the last half of this century, the debate in this field has centered around the "Falsification" methodology of Karl Popper. In testing deterministic hypotheses, Popper observed that hypotheses can be proven wrong with falsifying evidence by applying the logical rule of modus tollens. However, when experimental evidence is consistent with the hypothesis, believing that this confirms the hypothesis is committing the "affirming the consequent" fallacy. Hence, scientific "theories" cannot be verified, but only falsified. Subsequent philosophers pointed out a number of shortcomings with this method: it cannot be applied to statistical hypotheses (which is the area were methodology is most important to practicing scientists), the Duhem problem- an observation can only falsify a hypothesis if it is conclusively certain, and the problem of auxiliary assumptions- when data is inconsistent with the hypothesis, the test is not informative regarding whether the main hypothesis is false or whether the problem resides in the ever-present auxiliary assumptions that are necessary to connect the hypothesis with experimental implications.

Subsequent philosophers addressed these methodological faults by applying the principles of Bayesian statistics to the problem of testing hypotheses; using experimental evidence to transform an a priori probability of a hypothesis being valid to an a posteriori probability. Ideas from a number of Bayesian philosophers were synthesized by Howson and Urbach in their excellent book, Scientific Reasoning: The Bayesian Approach. This method applies equally well to both statistical and deterministic hypotheses. Also, the Bayesians claim neat solutions to other problems with Popper, e.g., auxiliary assumptions and Duhem. Predictably, other philosophers were quick to find faults with this approach. The most common objections are: the necessity of subjective prior probabilities and the "Problem of Old Evidence."

Mayo, in her book Error and the Growth of Experimental Knowledge, presents an Error-Statistical approach to scientific inference. She confronts head-on three important issues that any normative method must address: What counts as experimental evidence?, How to assess the acceptability of auxiliary assumptions and how to rule out alternative hypotheses?, and How to falsify statistical hypotheses? The first is addressed by explicitly identifying and justifying assumptions of the experimental data. The second issue is handled by the careful design and control of experiments. Statistical hypotheses are falsified using Neyman-Pearson type statistical tests.

The concept of "severe tests" is developed which is very powerful and widely applicable. Experiments are designed to ensure that the test of a hypothesis is severe and informative. These tests are useful in both testing auxiliary hypotheses and ruling out alternate hypotheses. A hypothesis can be confirmed if it passes a severe test (enabling the "Growth of Knowledge.") Mayo effectively uses the concept of severe tests to shed light on the philosophical problem of the acceptability of ad hoc (use-constructed) hypotheses. Common or canonical types of error are assembled into an "error repertoire" which are used to design experiments. When hypotheses fail, these well design tests will yield information as to which type of error was committed. Failed hypotheses are subsequently improved by applying the Peircean idea of "listening to error patterns."

Deterministic hypotheses are brought into this framework by the observation that in testing such hypotheses, inevitable approximations, inaccuracies and uncertainties enable the application of standard statistical tests. Also, Mayo's Error Statistical approach seems to address many of the other shortcomings of both Popper and the Bayesians. Perhaps the most important aspect of Mayo's methodology to practitioners as myself, is that these principles are of practical use to scientists, as opposed to merely impressive theoretical constructs. Her statement "you cannot just throw some evidence at the error-statistician and expect an informative answer" rings true to any scientist that has tried to draw conclusions from poorly designed experiments. I can strongly recommend this book to practitioners of both the "hard" sciences and certainly the "soft" sciences where methodology is of critical importance.

D.S. Fraedrich

Research Physicist

Naval Research Laboratory

This book is a good introduction to wavelets and their statistical applications. The book gives an elementary introduction to wavelets, including filter-bank interpretations. Not very technically advanced from my point of view (PhD in harmonic analysis), but a good book nonetheless.

i FOUND IT VERY HELPFUL AND FABULOUS FOR THOSE WANT TO APPROCH THE MEDICAL STATISTICS OR WRITING THEIR THESIS.

This is the first statistics textbook that I have actually understood and enjoyed, and I have read many of them. The authors take students through the material step by step and do not make assumptions about prior knowledge. The charts and examples are very helpful and clearly presented. As textbooks go, this is the best!

The Essentials of Stochastic Finance: Facts, Models, Theory by Albert N. Shiriaev, et al offers a clear treatment of both theoretical and emperical Finance. Shiryaev presents not only the essentials of probability as it is applied to finance,but he also covers recent develpoments in Mathematical Finance. It is very well written and it can be covered in one year (depending on the audience). Each topic moves from the specific to the general, beginning with one or more examples to lead into the theoretical results. This is the most comprehensive book out there. It covers Mathematical Finance, Martingale, Markov Thoery... to Econometric ARCH GARCH FGARCH ...to theory of Finance CAPM APT... PART II of the book requires a good knowledge of Stochastic Calculus at Karatzas and Shreve level...
Outstanding...

Evolutionary operation is a strategy for experimentation during production. It is a terrific method for continuous process improvement which is now popular in the new quality movement. Surprisingly, though very practical the approach never caught on when the book originally came out! Box and Draper are famous professors of statistics in the Statistics Department at the University of Wisconsin. Both are excellent writers and each has coauthored other very highly regarded texts.

These techniques are worth considering by anyone involved in producing high quality manufacturing processes. This is the only text that covers the topic.

I had used this book in spring 2000 . I was quite impressed by the detailed explanation this book cover. It was for the first time that I was using Excel for statistic class. This book cover various functions of statistics in such easy and simple manner that I was thrilled to see my own performance with Excel .

I would recommand this book for any student want to learn business Stats using Excel.