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Free eBook Mathematical Statistics (Chapman Hall/CRC Texts in Statistical Science) download

by Keith Knight

Free eBook Mathematical Statistics (Chapman  Hall/CRC Texts in Statistical Science) download ISBN: 158488178X
Author: Keith Knight
Publisher: Chapman and Hall/CRC; 1 edition (November 24, 1999)
Language: English
Pages: 504
Category: Math Science
Subcategory: Mathematics
Size MP3: 1270 mb
Size FLAC: 1744 mb
Rating: 4.2
Format: mbr lit rtf doc


mathematical statistics texts Product details. Series: Chapman & Hall/CRC Texts in Statistical Science (Book 46). Hardcover: 504 pages

mathematical statistics texts. -M. S. Ridout, Institute of Mathematics and Statistics, University of Kent at Canterbury, UK in Biometrics "one of the five best textbooks on a beginning course on theoretical statistics providing a good grasp on the foundations of theoretical statistics. Primarily for graduate students with mathematical backgrounds in linear algebra, multivariable calculus, and some exposure to statistical methodology. Highly recommended for all academic libraries. Hardcover: 504 pages.

Traditional texts in mathematical statistics can seem - to some readers-heavily weighted with optimality theory of. .Mathematical Statistics stands apart from these treatments

Traditional texts in mathematical statistics can seem - to some readers-heavily weighted with optimality theory of the various flavors developed in the 1940s and50s, and not particularly relevant to statistical practice. Mathematical Statistics stands apart from these treatments

Stochastic Modeling and Mathematical Statistics is a new and welcome addition to the corpus of undergraduate statistical textbooks in the market.

Stochastic Modeling and Mathematical Statistics is a new and welcome addition to the corpus of undergraduate statistical textbooks in the market. The singular thing that struck me when I initially perused the book was its lucid and endearing conversational tone, which pervades the entire text. In my course at the University of Michigan, I rely primarily on my own lecture notes and have used Rice as supplementary material.

Theory of Spatial Statistics: A Concise Introduction presents the most important models used in spatial statistics, including random fields and point processes, from a rigorous mathematical point of view and shows how to carry out statistical inference.

The mathematical background necessary for this book is multi- variate calculus and linear algebra; some exposure to real analysis (in particular, - proofs) is also useful but not absolutely necessary.

Knight - Mathematical Statistics . df - MATHEMATICAL. School University of New South Wales. Course Title MATH 2931.

Traditional texts in mathematical statistics can seem - to some readers-heavily weighted with optimality theory of the various flavors developed in the 1940s and50s, and not particularly relevant to statistical practice. Mathematical Statistics stands apart from these treatments. While mathematically rigorous, its focus is on providing a set of useful tools that allow students to understand the theoretical underpinnings of statistical methodology.

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MATHEMATICAL STATISTICS. Mathematical statistics, Keith Knight. p. cm. - (Texts in statistical science series) Includes bibliographical references and index.

Traditional texts in mathematical statistics can seem - to some readers-heavily weighted with optimality theory of the various flavors developed in the 1940s and50s, and not particularly relevant to statistical practice. Mathematical Statistics stands apart from these treatments. While mathematically rigorous, its focus is on providing a set of useful tools that allow students to understand the theoretical underpinnings of statistical methodology.The author concentrates on inferential procedures within the framework of parametric models, but - acknowledging that models are often incorrectly specified - he also views estimation from a non-parametric perspective. Overall, Mathematical Statistics places greater emphasis on frequentist methodology than on Bayesian, but claims no particular superiority for that approach. It does emphasize, however, the utility of statistical and mathematical software packages, and includes several sections addressing computational issues.The result reaches beyond "nice" mathematics to provide a balanced, practical text that brings life and relevance to a subject so often perceived as irrelevant and dry.

Features

Provides the tools that allow an understanding of the underpinnings of statistical methods Encourages the use of statistical software, which widens the range of problems reader can consider Brings relevance to the subject-shows readers it has much to offer beyond optimality theory Focuses on inferential procedures within the framework of parametric models, but also views estimation from the nonparametric perspective Solutions manual availalbe on crcpress.com
User reviews
Qusicam
The content is great. In my copy though, the inner margin is at places non-existent, which makes it difficult to read the book without breaking it in two. That's unfair for a book that costs more than 60£.
Sorryyy
With so many good graduate texts available to choose from I think it is more helpful for me to tell you what this offers that some of the others do not and then you can decide for yourself if it is for you. It is not the best written text and is highly mathematical. It does introduce some modern concepts such as empirical likelihood,the jackknife, and the bootstrap and devotes a chapter to the important topic of generalized linear models. However, the bootstrap is only mentioned briefly as a resampling tool that can be used in the context of generalized linear models. Its more general use as a competitor to the jackknife for estimation of variance or standard errors and in the context of confidence intervals is not mentioned.

An avantage of the book for some is that it includes chapters on probability theory. It only includes the topics needed to understand distribution theory for inference purposes and to be able to understand asymptotic distributions. To achieve this Knight covers the basic rules of probability and its measrue theoretic basis in chapter one. Inportant convergence results needed to develop the asymptotic theory of statistics is cover in Chapter 3 where the concepts of convergence in probability and convergence in distribution are given along with key results such as the weak law of large numbers and the central limot theorem. This is a nice feature as it makes the book self-contained and the student does not need a book or separate course in advanced probability since the essence of that material is covered in Chapters 1 and 3.

I do applaud the author for covering important topics such as generalized linear models along with the standard parametric theory of hypothesis testing and estimation. Another topic not commonly covered in the affect of model mispecification on results.

So if you are interested in learning about the jackknife, empirical likelihood and the Bayesian approach, this is one of very few advanced books that covers all these topics and still covers probability and standard statidtical theory (e.g. Cramer-Rao inequality, Lehmann-Scheffe theorem, Neyman Pearson lemma, uniformly most powerful tests, Basu's lemma, and sufficiency and efficiency concepts.
Terr
While statistics is a very interesting subject and can be taught in many interesting ways, it is by no means taught in any student friendly way in this book. To begin with, this book is about as interesting of a read as its cover design. It starts off well with some review of general concepts, but then as you go on, things that could and should be explained are skipped using phrases such as "it could be clearly shown that..." or "it is easy to see that...". Unfortunately, to the average student of statistics, not everything is trivial. Aside from that, there are many typing errors and by that i mean A LOT. This book can be greatly improved first with the fixing of all those typos, and second with better explanations and less assumptions of "clearity" to the reader. Also, the discussion should be made less dry by perhaps relating what is taught to some familiar concepts. Overall, unless you've been studying statistics for a while, dont expect to know what you're reading at all times.
Fecage
I used this book in a class that I took for intermediate statistics, a mid-level graduate course in the statistics department at a top university. This is an advanced book on mathematical statistics. In general, I found that if I had significant exposure to a topic previously, I could easily understand the text. Sections of the book containing topics new to me, however, were difficult to understand. The biggest failure of this book to me is that it serves as a poor reference tool. Definitions, theorems, and important concepts are often hidden in paragraphs nestled between examples. There is no appendix or list of distributions, moment generating functions, etc. The index is incomplete. A second negative aspect of the book is that there are many typos. The extent of the typos often increases the difficulty of understanding the text.
Overall, this book should never be used for an undergraduate course. I also do not recommend this book for use in a graduate course, unless some later edition with heavy revisions is released. I strongly recommend not buying this book for personal use as a reference tool.
Mayno
This book is very challenging for a beginner student in statistics. I am not sure how it would be if one had some preparation in the subject, but I still think that it should rather be used as a source for excercises and some concepts. However, using it can be a little frustrating sometimes. Let me give you an example: If you want to do some exercise which requires you to know a density function of a certain random variable then you have to look somewhere else because this book fails to provide you with a table of most common distributions. Also there are a lot of typos. Some exercises overcomplicate things, e.g. they are not about understanding a concept but giving you hard calculations to do. OK, as far as positive things about it: Some examples are good, definitions are precise, and most of exercises will make you think. Overall, I would recommend it only if you have mastered the basics. It would probably be useful with an excellent teacher who would explain the concepts to make you look at the material from a different point of view.
Saberblade
I checked several statistics books(Casella, Degroot and Wasserman), but this is the best one. Easy to read, and comprehensive. Graduate students who are not so good at math can read comfortably.