An Introduction to Mathematical Statistics and Its Applications

The seller is misleading the buyers. He or she shows the 6th edition and when you add to the cart it shows the 3th edition.

I am surprised by the number of negative reviews for what I consider to be a nicely written, well thought out, and logically presented introductory course on mathematical statistics. Yes, a working knowledge of elementary calculus is a prerequisite. But the mathematics invoked in the exposition of concepts and theorems are kept as simple as possible while maintaining that modest level of rigor appropriate for a introductory exposition. If you do not have the minimal mathematical prerequisites (such as freshman calculus), blame your instructor or your school for selecting an inappropriate text. But don't blame the authors! I thought the examples and problems were appropriate in their level of difficulty (mostly not so hard) and the relation to the material just covered. There are plenty of poorly written, impossibly dry, inpenetrable texts on statistics out there - this is not one of them. In addition, the book is attractively packaged, the paper quality is excellent, the visuals are informative and clearly presented - that also should not be taken for granted. Lastly the authors have a wicked entertaining sense of humor that spice the presentation throughout. I consider this book to be a welcome addition to the set of modern textbooks available to the curious serious student of probability and statistics.

The first half of the book begins with basic discrete and continuous probability theory. It continues with thorough overviews of the basic distributions (normal, Poisson, binomial, multinomial, chi-squared and student-T). The focus is on basic probability and variance analysis, though it briefly covers higher-order moments. The second half of this book is devoted to hypothesis testing and regression. There is an excellent explanation of the mathematical presuppositions of the various classical experimental methodologies ranging from chi-square to t-tests to generalized likelihood ratio testing. It contains a very nicely organized chapter on general regression analysis, concentrating on the common least squares case under the usual transforms (e.g. exponential, logistic, etc.). Like many books in mathematics, this introduction starts from first principles in the topic it's introducing, but assumes some "mathematical sophistication". In this case, it assumes you're comfortable with basic definition-example-theorem style and that you understand the basics of multivariate differential equations. I was a math and computer science undergrad who did much better in abstract algebra and set theory than analysis and diff eqs, but I found this book extremely readable. I couldn't have derived the proofs, but I could follow them because they were written as clearly as anything I've ever read in mathematics. I found the explanation of the central limit theorem and the numerous normal approximation theorems for sampling to be exceptionally clear. The examples were both illuminating and entertaining. One of the beauties of statistics is that the examples are almost always interesting real-world problems, in this case ranging from biological (e.g. significance testing for cancer clusters) to man-made (e.g. Poisson models of football scoring) to physical (e.g. loaded dice). The examples tied directly to the techniques being explored. The exercises were more exercise-like in this book than in some math books where they're a dumping ground for material that wouldn't fit into the body of the text. This book has clearly been tuned over many years of classroom use with real students. I read this book because I found I couldn't understand the applied statistics I was reading in machine learning and Bayesian data analysis research papers in my field (computational linguistics). In paticular, I wanted the background to be able to tackle books such as Hastie et al.'s "Elements of Statistical Learning" or Gelman et al.'s "Bayesian Data Analysis", both of which pretty much assume a good grounding in the topics covered in this book by Larsen and make excellent follow-on reading.

This book manages to stay focused on the main ideas all the way through. It uses no more math than what is necessary to derive the proofs of most theorems (although some are omitted). The main ideas of each chapter is introduced before the details are worked out, and summarized at the end of each chapter. The examples and case-studies are usually interesting (sometimes thought-provoking), instead of solely being based on urns and coloured balls. And the exercises range from trivial to interesting...In short, this is about as good as a textbook gets...

This book is very useful in learning statistics. The text has many applications and problems that are practical. The authors include interesting biographical background on certain mathematicians and topics--this helps the student feel more connnected with the material. I highly recommend buying a copy (especially used--better deal) of this book for class or personal study.

This is an excellent introductory text for students who wish to know more about mathematical statistics. The book is ideal for a student with a good solid year of mathematics, and the case studies really make the statistics theory more relevant