This book removes all those fearsome notions about statistics. The topics presented are sequential, well organized and easy to understand.Case studies are helpful in associating learned concepts to real world applications. Examples from various statistical packages give students a flavor of how computer generates statistical reports.Examples with solutions guide students toward step by step understanding of concepts involved. Overall, this is an excellent book for all those students who really want to get serious about statistics.

This is a marvelous primer on measure-theoretic probability. I came across it a couple of years after taking a course based on Chung's famous text ("A Course in Prob. Theory") and found it to be an excellent book for review and remediation--that is, it helped me get a better overview of the material I had already learned and it helped me learn topics such as, say, uniform integrability, that didn't sink in too well the first time around.

According to the preface, the author prepared most of the book as supplemental class notes for the benefit of his students in a course whose main text was, if I recall correctly, Billingsley's excellent "Probability and Measure". The students were so enthusiastic about the usefulness of Professor Rosenthal's supplemental info that they insisted he publish it, despite his objection that the book wasn't original enough to warrant entry into an already crowded field. Well, the students made the right call: Rosenthal's clear and concise text will, I think, help almost any student learn measure-theoretic probability more efficiently. I'd also recommend it to folks who need a concise review of measure-theoretic probability.

The book is up-to-date and advanced where materials cannot be found from some other general time series texts.

It is an excellent book for understanding time series analysis, well written and clearly explained. Readers should have some knowledge of univariate ARIMA model but no expertise is required. The book focuses on how, why, and when to combine the effect of other variables and the univariate ARIMA model. The suggested methods are demonstrated by a well-explained example. The author describes each step in the analysis in detail, this greatly helps in understanding the logic. Therefore, I highly recommend this book for students or persons who have a preliminary knowledge of the time series analysis technique, and who want to develop a more in-depth understanding of the subject. In addition, it is a must-have reference book for all time-series analyst.

It is a good statistics book. My teacher wrote it. He is a good teacher. I really want an A+!

As a time series analyst who studied time series in graduate school when the first edition of Bloomfield's book came out, I really appreciated it. The frequency approach to time series is not natural because we tend to look at the series to see how it changes (fluctuates) over time. This makes the time domain approach very natural. It is only when one sees periodicity in the series and can relate the series to a summation of sine and cosine waves that the frequency domain ideas get appreciated.

Bloomfield introduces Fourier series in a gentle way, making it clear. Concepts are introduced as needed to deal with specific aspects of real time series data. I particularly liked his treatment of complex demodulation, which could be a very difficult concept to grasp if not presented carefully. But Bloomfield explains it well and provides another way to look at the famous Wolfer sunspot data using an approach based on complex demodulation (assuming a slowly changing period for the series).

This is a very nice collection of papers: tutorials, and well written research presented to a wide audience. They cover stochastic aspects, geometry, analysis, iterated function systems, algorithms, to mention just a few. And they are written by authorities in the field. Fractals make headlines from time to time[--are they everywhere?], and and they make lovely color pictures; but they are also part of a substantial mathematical theory, one with an exciting mathematical history. This very important book presents the subject in a way that it can be taught to students. Or it can be used for selfstudy! In view of the many applications to geometric analysis, to PDE, and to statistics, it is likely that fractal geometry will soon be a standard math course taught in many (more) math departments. A recent example of this: By now it is widely recognized that the selfsimilarity aspects of the wavelet algorithms are key to their sucess. The authors have produced an attractive book on fractals. There are others, for example one by Falconer from 1985[The geometry of fractal sets] with a slightly more potential theoretic bent.

Dr. Driebe's book, which grew out of his work at the Ilya Prigogine Center for Studies in Statistical Mechanics, is an excellent overview of the theory of irreversible systems in physics. It is suitable for an advanced graduate-level physics course, and the reader who completes it would be capable of doing state-of-the-art research into this branch of nonlinear dynamics.

This and its precursor volume on functional equations by Aczel are the world's greatest books on functioal equations in my opinion. Functional equations are among the most general equations in mathematics, and therefore anybody who wants to use equations however remote from physical sciences should buy and read these books (with the help of a consultant and/or tutor if necessary to explain them in approximately ordinary English). To give the reader an example, logarithms obey log(a times b) = log a + log b. It turns out that the form of this equation, f(a times b) = f(a) + f(b), characterizes logarithms uniquely under certain fairly general conditions. Functional equations like the last one turn out to uniquely characterize whole fields and categories of both commonly and uncommonly used equations ranging from probability and statistics to information theory and entropy and beyond. For example, how many people know that the bell-shaped curve (called the normal or Gaussian distribution) used in grading students on a curve or average has its own functional equation? Or how many people know that computer or cryptographic codes cannot be shorter than Shannon entropy for average codeword length because of its functional equation properties? Aczel and Dhombres cover these and many other topics. The book is published by Cambridge University Press, which is one of the world's best publishing companies, and Aczel is at Waterloo University in Canada, while Dhombres is (at least, until recently) at a French university. Waterloo and most French universities are among the best in the world.

This is a marvelous introductory statistics book. In the first five chapters Wilcox presents the classical statistical methods with simplicity and great care. Emphasis is properly placed on the assumptions of the methods and the fact that many times important assumptions are violated in practice. This is all done in the first five chapters. Then in chapters 6 - 12 he provides alternative modern methods that can work better than the classical ones when the assumptions fail.

Chapter 6 is on bootstrap. Chapter 8 covers robust measures of location. Chapter 9 covers statistical inference based on robust measures of location. Chapter 10 deals with nonparametric and robust measures of association. Chapter 11 covers robust estimates of location and Chapter 12 "Alternative Strategies" includes rank test, permutation tests and multiple comparison issues.

At the beginning of Wilcox's book there are three precious quotes that you should appreciate. 1. "If at first the idea is not absurd, then there is not hope for it" Albert Einstein. 2. "Everyone believes in the [normal] law of errors, the experimenters because they think it is a mathematical theorem, the mathematicians because they think it is an experimental fact." Henri Poincare. 3. "Each generation that discovers something from its experience must pass that on, but it must pass that on with a delicate balance of respect and disrespect, so that the race ... does not inflict its errors too rigidly on its youth, but it does pass on the accumulated wisdom plus the wisdom that it may not be wisdom." Richard Feynman.

These quotes were well chosen to fit the theme of the book and represent the wisdom of three great men from the 19th and 20th Centuries.