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All of Statistics Suitable for those who want to learn probability and statistics quickly, this book brings together many of the main ideas in modern statistics. It includes modern topics like nonparametric curve estimation, bootstrapping and classification, topics that are usually relegated to follow-up courses. Full description

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Détails sur le produit

  • Relié: 442 pages
  • Editeur : Springer-Verlag New York Inc.; Édition : 1st Corrected ed. 2004. Corr. 2nd printing 2004 (15 septembre 2004)
  • Collection : Springer Texts in Statistics
  • Langue : Anglais
  • ISBN-10: 0387402721
  • ISBN-13: 978-0387402727
  • Dimensions du produit: 15,6 x 2,5 x 23,4 cm
  • Moyenne des commentaires client : 5.0 étoiles sur 5  Voir tous les commentaires (1 commentaire client)
  • Classement des meilleures ventes d'Amazon: 113.635 en Livres anglais et étrangers (Voir les 100 premiers en Livres anglais et étrangers)
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Première phrase
Probability is a mathematical language for quantifying uncertainty. Lire la première page
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Couverture | Copyright | Table des matières | Extrait | Index | Quatrième de couverture
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Format: Broché
Ce livre est un résumé "rapide" en inférence statistique. Les explications sont courtes et claires.

Je ne suis pas sur que ce livre puisse intéresser ceux issus d'une formation initiale en statistiques pure. Par contre, pour ceux qui, comme moi, ont travaillé sur une thèse de doctorat ayant besoin de traiter un sujet avec un regard rigoureux sur les données obtenues et analysées, ce livre tombe on ne peux pas mieux. A la fin, il y a une bibliographie que l'on peut utiliser pour aller plus loin et approfondir les points souhaités.

Ce livre a un companion du même auteur : "All of Non parametric statistics", écrit avec la même démarche.All of Nonparametric Statistics

OBS : ce commentaire est une reprise du commentaire fait pour son companion, et valable pour les deux.
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Commentaires client les plus utiles sur (beta) 26 commentaires
65 internautes sur 67 ont trouvé ce commentaire utile 
all of statistics in just this little book? 6 avril 2008
Par Michael R. Chernick - Publié sur
Format: Relié
Wasserman wrote a book titled "All of Nonparametrics." You can see my review of that on amazon. That also was a concise treatment of the subject in a book that covered more topics than say Conover's fine book but yet in less pages. The trick was to give the basics,provide references and offer the reader a broad perspective on the topic without going through the nitty gritty details. I was impressed at the way the author achieved his goal and addressed topics like nonparametric regression and wavelets that are not normally covered in a first course in nonparametrics.

Covering all of statistics in just slightly more pages seems at first an insane notion. The approach is the same as in the other book but with so much more to cover the treatment is a little less detailed and a little more concise. The reader needs to realize that the title is intentionally misleading. In both cases it is not Wasserman's intention to really cover every aspect of the subject at hand. Rather it is a carefully chosen selection of essential topics written in a concise but still very clear and lucid way. I think a more appropriate title would have been "All You Really Need to Know About Statistics That You Were Afraid to Ask." I think the author might consider such a change of title in a revised edition. I would have the same typr of title change for the Nonparametrics book as well. These books are different from the standard fare for introductory texts. But if you want a overview of the subject where the author points you in the right direction for dotting the i's and crossing the t's, this is the right book for you. For practitioners who are not statisticians this usually what they are looking for. For statisticians it is a useful reference source to go along with other texts on statistical inference.
37 internautes sur 38 ont trouvé ce commentaire utile 
A very accessable survey of many modern statistical techniques 18 octobre 2005
Par a reader - Publié sur
Format: Relié
This book provides a survey of many modern statistical techniques such as bootstrapping and modern classification methods, as well as presenting the fundamentals of inferential theory. The book appears to be aimed at an audience conversant in mathematics, but more interested in a general overview of methods than rigor and limit theorems. As such, it presents brief and readable introductions to topics such as support vector machines, kernel estimation and Markov Chain Monte Carlo Methods that usually only appear in more specialized literature. On the whole I found it a very useful and readable text. A minor criticism is that there are a fair number of typographical errors, especially in equations in the later chapters; presumably this will be fixed in subsequent editions.
31 internautes sur 32 ont trouvé ce commentaire utile 
well written 13 décembre 2004
Par Jump - Publié sur
Format: Relié
This is a very well written book. Does a good job of reviewing the fundamental concepts and also hitting on advanced topics, has well chosen examples and problems, and is clearly organized and written.

This is a good choice for a computer scientist who is getting into statistics for the first time or needs a refresher. It would also be a very good choice for self study.

The level of this book is approximately that of "Pattern Classification" (also a good book) or the slightly more advanced "The Elements of Statistical Learning" (which I would not recommend).
39 internautes sur 42 ont trouvé ce commentaire utile 
Excellent at times, but only a summary or introduction: far from thorough 2 octobre 2007
Par Alexander C. Zorach - Publié sur
Format: Relié Achat vérifié
This book is essentially a summary of the major theoretical topics in statistics, at an introductory level. The focus is on theory, not on data analysis or modeling, but there are more connections to data analysis and modeling than is typical among books on the same topics. The main flaw in this book is not that it does anything poorly, but rather, that it omits a lot.

The book is very balanced in its coverage of different topics, its discussion of the frequentist vs. Bayesian paradigm, etc. It mentions parametric and nonparametric inference, including hypothesis testing, point estimation, Bayesian inference, decision theory, regression, and even two different approaches to causal inference. The book also paints a fairly whole picture of how the different topics relate to each other and fit into a unified theoretical framework. Another huge strength of this book is that it always omits unnecessary technical details, including only streamlined discussions highlighting essential points.

The main weakness of this book is that certain topics are only brushed upon and not adequately explained. The first two chapters are deep enough for students to get a more or less complete understanding of the important ideas (assuming they do the exercises). But, for example, the 4th chapter covering inequalities is simply a collection of equations and formulas: the text explains how to use them, but not where they come from or what their intuitive interpretation is. This problem arises throughout the book but it is most evident in chapter 4. I want to remark, however, that this problem is widespread in statistics textbooks, and this book is still less lacking in this respect than is common among typical texts.

I'm not sure this book makes the best textbook. In my opinion most students would benefit from a text that offers more explanation of the meaning and driving ideas behind theory. However, I like the way this book gets to the main points quickly and omits confusing and tedious details and irrelevant tangents. This book may be good for students who are briefly studying statistics and will never take a future course. This book is useful as a very basic reference, but I think its best use is for self-study--advanced students will find it one of the quickest and best ways to get an overview of most of the fundamental topics in theoretical statistics.

Honestly, I think Wasserman is an outstanding writer, and part of me wishes he would expand this book to the scale of something like Casella and Berger's "Statistical Inference", covering more material and adding more discussion of certain topics, but retaining the style of being to-the-point and omitting tedious details. I think this is one of the best books of its type out there but I refrain from giving 5 stars because I think Statistics is one area where most of the 5 star books have not yet been written.
21 internautes sur 23 ont trouvé ce commentaire utile 
Great for a quick summary of the basics 1 mars 2006
Par Bruce G. Lindsay - Publié sur
Format: Relié Achat vérifié
I have not read every section, but have found that it is a nice place to get a quick summary of the main results in some of the more outlying regions of statistics. I would not use it for a course because of its brevity, but I have recommended it to my class of future statisticians as a nice capsule reference book.
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