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Kendall's Advanced Theory of Statistics, 3 Volume Set, by Alan Stuart
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This major revision contains a largely new chapter 7 providing an extensive discussion of the bivariate and multivariate versions of the standard distributions and families. Chapter 16 has been enlarged to cover mulitvariate sampling theory, an updated version of material previously found in the old Volume 3. The previous chapters 7 and 8 have been condensed into a single chapter providing an introduction to statistical inference. Elsewhere, major updates include new material on skewness and kurtosis, hazard rate distributions, the bootstrap, the evaluation of the multivariate normal integral and ratios of quadratic forms. This new edition includes over 200 new references, 40 new exercises and 20 further examples in the main text. In addition, all the text examples have been given titles and these are listed at the front of the book for easier reference.
- Sales Rank: #3717650 in Books
- Published on: 2010-08-30
- Original language: English
- Number of items: 1
- Dimensions: 9.90" h x 4.80" w x 7.00" l, .0 pounds
- Binding: Hardcover
- 700 pages
Review
'As a comprehensive resource, ... it is unsurpassed.' -- International Statistics Review 'The general authority of this work, together with its lasting value as a reference source, make it the Bible which no statistician should be without.' -- The Statistician 'Will occupy a central place in the statistical literature.' -- Nature
From the Back Cover
The development of statistical theory in the past fifty years is faithfully reflected in the history of the late Sir Maurice Kendall's volumes "The Advanced Theory of Statistics." The Advanced Theory began life as a two volume work (Volume 1, 1943; Volume 2, 1946) and grew steadily, as a single authored work until the late fifties. At that point Alan Stuart became involved and the Advanced Theory was rewritten in three volumes. When Keith Ord joined in the early eighties, Volume 3 became the largest and plans were developed to expand it into a series of monographs called the "Kendall's Library of Statistics" which would devote a book to each of the modern developments in statistics. This series is well on the way with 5 titles in print and a further 7 on the way. A new volume on Bayesian Inference was also commissioned from Tony O'Hagan and published in 1994 as Volume 2B of the Advanced Theory. This Volume 2A is therefore the completely updated "Volume 2 - Classical Inference and Relationship." A new author, Steven Arnold, was invited to join Keith Ord and they have between them produced a work of the highest quality. References have been updated and material revised throughout. A new chapter on the linear model and least squares estimation has been added.
About the Author
Alan Stuart is formerly Professor Emeritus of Statistics at the University of London, UK.
Keith Ord os Professor of Decision Sciences at Georgetown University, USA.
Most helpful customer reviews
11 of 14 people found the following review helpful.
The relationship between populations and statisitics and the parameters used to characterize them
By Ulfilas
As a grad student, this 472 page first volume of Kendell and Stuart was the book I relied on in order to learn how to calculate the unbiased estimator of a population of statistical data. In elementary texts on statistics and data reduction you are given an inkling of this problem with regard to calculating a quantity such as the mean of a finite distribution. A real distribution differs from an ideal distribution in that its number of elements is finite rather than infinite. In order to compensate for the fact that the real distribution contains as few as N elements, the sum of a given value (e.g. position) for each element is divided by N-1 (instead of N for an ideal or infinite distribution) in order to better estimate the mean. In order to properly compensate for the finite number of elements of a real distribution, however, one needs to calculate the unbiased estimator of that distribution. The books teaches the reader the complex techniques, concepts, and statistical and populations parameters that are used in compensating for the finite nature of real data.
The more general focus of this book is that of distribution theory, a discipline dedicated to describing the statistical distribution of the values associated with the members of any group of individuals or events, be they atoms, workers in a given industry, deaths due to smallpox, or pencils in a can on your desk. The concept of population (sometimes called a parent population) is defined as an potentially uncountable or infinite set of such events or individuals, while statistics correspond to the finite set of events or individuals that correspond to actual data.
In order to bridge this gap between the idealized world of parent populations and the statistical data that they beget, Kendall and Student introduce the reader to a variety of mathematical tools, some of which are used to characterize parent populations; while others belong to the realm of statistics. In addition to the familiar moments characterizing populations such as the mean, the authors develop the concept of cumulants, which are the logarithmic analogues of moments. Being an logarithmic entity, the cumulant is independent of the choice of origin. As a result, by expressing a moment in terms of cumulants, the researcher is able to set the origin to zero and thereby allow odd moments to assume the value of zero--thus greatly simplifying mathematical expressions that correspond to a sum of such moments. The expectation value of a product of cumulants can then, in turn, be expressed as a k-statistic, which can be formulated in terms of augmented symmetric functions. Augmented symmetric functions are statistics that are merely the sum of products. Each such product can be broken in to simple moments that are referred to as power sum statistics.
One therefore proceeds as follows: express the quantity you wish to estimate in terms of the sum of products of parent moments. Express each parent moment as a sum of cumulant products. Now that your quantity corresponding to you parent population is expressed as a sum of cumulant products, you are ready to determine that statistic that is its unbiased estimator. The unbiased estimator of each cumulant product is equal to a k-statistic. Each k-statistic is expressible as a sum of augmented symmetric functions. Each augmented symmetric function is expressible as a sum of products of power sums. The final result is the statistic that is the unbiased estimator of your parent quantity expresses as a sum of products of power sums.
I should note that it is my experience that the need for the complicated mathematical machinery discussed in this book is not always obvious when first calculating a statistical quantity, which often consists of a sum of moments to the second or forth power. The problem, however, has a tendency to become more difficult when the researcher needs to calculate the statistical variance of the quantity in question. If a given statistic includes a forth moment, for example, its variance will include an eighth moment. Calculation the unbiased estimator of this eighth moment will certainly require use and understanding of all of the population and statistical parameters discussed in this book.
2 of 2 people found the following review helpful.
A classic work on the theory of statistics
By Terence Mills
Many years ago, a statistician advised me that, as a statistician, I should have a copy of Kendall and Stuart on my bookshelves. So, I bought Volumes 1-3 of the third edition. It has been a very good investment. I'd like to sit down and read all three volumes. As the theory of statistics has developed, so has Kendall and Stuart's work. More recent editions have been developed by other authors - now the work is almost too large. It's a reliable source of theoretical ideas, written well by experts, with excellent bibliography. If I want to understand a theoretical concept in statistics, I often start with Kendall and Stuart. You won't find much advice on practical aspects of applied statistics, such as computer software. Reading Kendall and Stuart requires a strong background in mathematics because it does deal with "advanced" ideas as the title suggests. It is an expensive work; so you might check it out through a library and look through it before you decide to buy it.
1 of 2 people found the following review helpful.
Essentials
By Gary Bowers
If you are a graduate student in Statistics you will have been told to buy this set. Ignore this advice at your own peril. These three volumes cover all of the basic theory that you need to know prior to writing your thesis.
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