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Senin, 15 Desember 2014

[L102.Ebook] Ebook Free An Introduction to Multivariate Statistical Analysis, by T.W. Anderson

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Sales Rank: #7455114 in Books
Published on: 1962
Binding: Hardcover
374 pages

Most helpful customer reviews

59 of 59 people found the following review helpful.
classic text from 1958 revised
By Michael R. Chernick
The first edition of Ted Anderson's text on multivariate analysis was published in 1959. At the time it had no rivals. This book gives a thorough mathematical treatment of classical multivariate analysis. It is extremely well organized. Development of the multivariate normal distribution and its properties are given a thorough and rigorous treatment. The Wishart distribution is derived. Properties of the multivariate normal distribution are applied to problems of classification, principal components, canonical correlation and tests of hypotheses including the use of Hotelling's T square.
As a graduate student at Stanford, I audited Ted Anderson's multivariate analysis course, that he taught out of the first edition of the book. It wasn't until 1984 that he revised the text incorporating some new materials including the bootstrap method.

This is an advanced course for graduate students in statistics. It is the best source for a rigorous mathematical treatment of the important results from the theory of the multivariate normal distribution. However, it is not easy reading for someone who is interested in applications but does not have strong training in mathematics (particularly linear algebra). For applications and approaches when the normal theory doesn't apply, the book by Gnanadesikan is very good. There are now many good theoretical and applied texts on multivariate analysis including the text by Eaton, the one by Srivastava and Khatri, one by Rencher, one by Johnson and Wichern, and the one by Mardia, Kent and Bibby. Naik and Khattree have written a very nice applied multivariate book that demonstrates the applications using SAS software every step of the way.

There are now many subspecialties including cluster analysis, principal components, correspondence analysis, factor analysis and classification that have complete texts devoted to them.

Anderson has now published a third edition to this book and it incorporates bootstrap methods

8 of 8 people found the following review helpful.
Multivariate analysis for mathematicians
By Terence Mills
I wanted to know some mathematical details from multivariate analysis. Many books on multivariate analysis are written for those who are happy to skip all these details - and often, I too am in this category. But this time I wanted proofs and mathematical explanations. I found what I wanted in Anderson's classic text. It is a masterly work of scholarship. The author is an authority on the subject; his writing is clear - if you like reading mathematics; the proofs are there; the text contains several hundred exercises - and they are not all research level exercises; hardly any typographical errors as far as I can see. A reasonable background in linear algebra, multivariate calculus and mathematical statistics will be helpful in reading this book. I have been around long enough not to read too much into the word "introduction" used in the title. The book will not help you to learn how to use computer packages for multivariate analysis; this is a book about mathematics.

2 of 2 people found the following review helpful.
The standard in intoductory (mostly Guassian distibution) multivariate statistical analysis.
By CURE
What can U say, but Anderson is one of a couple of authors to have written seminal text on multivariate statistical analysis. One should have a background in univariate statistical analysis (e.g., Hogg, or possibly Rao). Although Anderson reviews matrix theory, at least a university level course is required. Required background also includes at least a full series of university-level calculus - multivariable and advanced - some complex variable theory would also be helpful as well as familiarity with advanced functions (e.g., Arfken). Also, it would be helpful to either read and/or have had a course in applied multivariable statistics (e.g. Johnson).

Pros:
It reviews both matrix algebra for statistics and univariate & linear models.
The text is thorough in the topics it covers.
It is terse in some of in its derivations - if that is your preference.
Anderson's MO is an excellent balance of both mathematical rigor, but not as stifling/limited as the standard Theorem-Lemma-Corollary, etc., format.

Cons:
It is lacking on examples and applications.
This addition de-emphases some of the more modern Vec/Vech-matrix calculus that is more common in, say, modern econometrics.
Many of the derivations would be easier with a background in Fourier analysis and KL/orthogonal-function expansions.
The tables are a bit lame.
Some theorem derivations would benefit from a tensorial representation.

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Kamis, 04 Desember 2014

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Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods--in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach.

Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probability and develops a step-by-step sequence of connected ideas, including basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and inference from single and multiple models. This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works. It also explains how to write out properly formulated hierarchical Bayesian models and use them in computing, research papers, and proposals.

This primer enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management.

Presents the mathematical and statistical foundations of Bayesian modeling in language accessible to non-statisticians
Covers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and more
Deemphasizes computer coding in favor of basic principles
Explains how to write out properly factored statistical expressions representing Bayesian models

Sales Rank: #178740 in Books
Published on: 2015-08-04
Original language: English
Number of items: 1
Dimensions: 9.30" h x .90" w x 6.20" l, .0 pounds
Binding: Hardcover
320 pages

From the Back Cover

"This pitch-perfect exposition shows how Bayesian modeling can be used to quantify our uncertain world. Ecologists--and for that matter, scientists everywhere--are aware of these uncertainties, and this book gives them the understanding to do something about it. Hobbs and Hooten take us on a signposted journey through the culture, construction, and consequences of conditional-probability modeling, readying us to take our own scientific journeys through uncertain landscapes."--Noel Cressie, University of Wollongong, Australia

"Hobbs and Hooten provide a complete guide to Bayesian thinking and statistics. This is a book by ecologists for ecologists. One of the powers of Bayesian thinking is how it enables you to evaluate knowledge accumulated through multiple experiments and publications, and this excellent primer provides a firm grounding in the hierarchical models that are now the standard approach to evaluating disparate data sets."--Ray Hilborn, University of Washington

"In this uniquely well-written and accessible text, Hobbs and Hooten show how to think clearly in a Bayesian framework about data, models, and linking data with models. They provide the necessary tools to develop, implement, and analyze a wide range of ecologically interesting models. There's something new and exciting in this book for every practicing ecologist."--Aaron M. Ellison, Harvard University

"Hobbs and Hooten provide an important bridge between standard statistical texts and more advanced Bayesian books, even those aimed at ecologists. Ecological models are complex. Building from likelihood to simple and hierarchical Bayesian models, the authors do a superb job of focusing on concepts, from philosophy to the necessary mathematical and statistical tools. This practical and understandable book belongs on the shelves of all scientists and statisticians interested in ecology."--Jay M. Ver Hoef, Statistician, NOAA-NMFS Alaska Fisheries Science Center

"Tackling an important and challenging topic, Hobbs and Hooten provide non-statistically-trained ecologists with the skills they need to use hierarchical Bayesian models accurately and comfortably. The combination of technical explanations and practical examples is great. This book is a valuable contribution that will be widely used."--Benjamin Bolker, McMaster University

"This excellent book is one of the best-written and most complete primers on Bayesian hierarchical modeling I have seen. Hobbs and Hooten anticipate many of the common pitfalls and concerns that arise when non-statisticians are introduced to this material. Researchers across a wide range of disciplines will find this book valuable."--Christopher Wikle, University of Missouri

About the Author
N. Thompson Hobbs is senior research scientist at the Natural Resource Ecology Laboratory and professor in the Department of Ecosystem Science and Sustainability at Colorado State University. Mevin B. Hooten is associate professor in the Department of Fish, Wildlife, and Conservation Biology and the Department of Statistics at Colorado State University, and assistant unit leader in the US Geological Survey's Colorado Cooperative Fish and Wildlife Research Unit.

Most helpful customer reviews

1 of 1 people found the following review helpful.
great book!
By C.Piper
Well written and easy to follow book for ecologist. This book explains Bayesian modeling from beginning to end in a way that makes to sense to those of us who are not statisticians.

0 of 0 people found the following review helpful.
Five Stars
By Brandi
Great!

0 of 3 people found the following review helpful.
Five Stars
By Nyssa J Silbiger
Wonderful Bayesian book!

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New in paperback! This book comes at a time when opera-lovers, singers, directors, and critics alike are taking a new look at the dramatic soprano heroines created by Giuseppe Verdi and Giacomo Puccini, endeavoring to delve beyond inherited scholarly interpretation and gain a richer understanding of these compelling female characters. Artistically limited by the bel canto musical tradition popular at the time, Verdi launched a new style dramma per musica which also demanded a new soprano archetype. This book illustrates the musical evolution of the Verdi and Puccini soprano while illuminating the dramatic scope and power of these great heroines.

Avoiding critical reductionism, Verdi and Puccini Heroines provides an unprecedented and probing discussion of how these great soprano roles were conceived and executed. Accordingly, the authors take a three-dimensional look at these heroines, examining seven operas: Il Trovatore, La Forza del Destino, Aida, La Boh�me, Tosca, Madama Butterfly, and Turandot. The chapters, which are fully self-contained analyses, contain translations, illustrative musical examples, supplementary notes, and references to each opera's literary sources. The musical analysis, while thorough, is descriptive and accessible to all levels of readers.

Sales Rank: #3354611 in eBooks
Published on: 2000-01-01
Released on: 2013-09-23
Format: Kindle eBook

Review
Well researched...offers new perspectives on these significant female characters and insight into the dramatic intent of Verdi and Puccini. (Journal Of Singing)

About the Author
Geoffrey Edwards is an award-winning author and stage director. Ryan Edwards, a leading baritone with the Metropolitan Opera, has received international acclaim as a singing actor.

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Sabtu, 29 November 2014

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Published on: 2015
Format: Audiobook
Binding: MP3 CD

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1 of 2 people found the following review helpful.
Total waste of my dollars
By Sachie Ramnauth
I briefly read this book online thinking it is was the synopsis. It was the book in its entirety. Furthermore, there was no substance or content...Total waste of my dollars

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Published on: 1900
Binding: Paperback

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Sabtu, 15 November 2014

[Y889.Ebook] Free PDF Learn Tamil in 30 days (National integration language series), by N Jegtheesh

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Sales Rank: #10470020 in Books
Published on: 1985
Binding: Unknown Binding
160 pages

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Kamis, 13 November 2014

[O604.Ebook] Free PDF A Scale Modeller's Guide to Aircraft of the Gran Chaco War, by Richard Humberstone

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A 40 page book in full colour featuring 45 marking schemes in 1/72 scale for the fighters, bombers, trainers and transport aircraft of the 1928-1935 Bolivian/Paraguayan conflict.

Sales Rank: #1543907 in Books
Published on: 2016-10-09
Original language: English
Number of items: 1
Dimensions: 10.00" h x .11" w x 7.99" l, .28 pounds
Binding: Paperback
40 pages

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