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by Alan Stuart

  • ISBN: 0470669543
  • Author: Alan Stuart
  • ePub ver: 1917 kb
  • Fb2 ver: 1917 kb
  • Rating: 4.8 of 5
  • Language: English
  • Pages: 700
  • Publisher: Wiley; 1 edition (August 30, 2010)
  • Formats: mbr lrf mobi doc
  • Category: Math
  • Subcategory: Mathematics
epub Kendall's Advanced Theory of Statistics, 3 Volume Set download

by M. G. Kendall (Author), Alan Stuart (Author). Kendall's Advanced Theory of Statistics: Volume 2A: Classical Inference and the Linear Model (Kendall's Library of Statistics).

by M. Kendalls Advanced Theory Of Statistics 6Ed Vol 1(Pb 2015). Alan Stuart & Keit. aperback. The Advanced Theory of Statistics. Volume 1: Distribution Theory.

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Maurice G. Kendall, Alan Stuart. This 3-volume set offers the complete, classic Kendall's Advanced Theory of Statistics in a single, value-for-money pack

Maurice G. This 3-volume set offers the complete, classic Kendall's Advanced Theory of Statistics in a single, value-for-money pack.

John Wiley and Sons, Inc. Book Format. Electrode, Comp-447755174, DC-prod-dfw5, ENV-prod-a, PROF-PROD, VER-30. 3, b900, 79e8de9ee0d, Generated: Sun, 17 Nov 2019 15:08:54 GMT.

Поставляется из: Англии Описание: Kendall& Advanced Theory of Statistics and Kendall& Library of Statistics The development of modern statistical theory is reflected in the history of the late Sir Maurice Kendall& volumes, The Advanced Theory of Statistics.

Kendalls Advanced Theory of Statistics and Kendalls Library of Statistics The development of modern statistical theory is. .Alan Stuart is former Professor of Statistics at the London School of Economics.

Kendalls Advanced Theory of Statistics and Kendalls Library of Statistics The development of modern statistical theory is reflected in the history of the late Sir Maurice Kendalls volumes, The Advanced Theory of Statistics. This landmark publication begain life as a two-colume work and grew steadily, as a single-authored work, until the late 1950s. This volume offers a major revision including a discussion of the bivariate and multivariate versions of the standard distributions and families. J. Keith Ord is Professor of Management Science and Statistics at Pennsylvania State University.

Kendall's Advanced Theory of Statistics and Kendall's Library of Statistics. The development of modern statistical theory is reflected in the history of the late Sir Maurice Kendall's volumes, The Advanced Theory of Statistics.

The Alan Stuart, J Keith Ord, The Maurice Kendall. The original three-volume Advanced Theory of Statistics has long been regarded as the definitive work on statistical theory. These two volumes now present the essential topics that every statistician needs to know. The two core volumes will be supplemented in due course by a volume on Bayesian Inference and by a series of monographs on more specialized topics.

Classic Kendall's Advanced Theory of Statistics. This exciting new volume takes a positive spin on the field of statistics. PREFACEThe need for a thorough exposition of the theory of statistics has been repeatedly emphasised in recent years. Statistics is seen by students as difficult and boring, however, the authors of this book have eliminated that theory Подробнее.

Kendall's Advanced Theory of Statistics, 2/E, Vo. B (Exclusive) (PB) by.Kendall's Advanced Theory of Statistics Volume 2A Classical Inference and the Linear Model. the late Sir Maurice Kendall; Alan Stuart; J. Keith Ord. Published by Oxford University Press, USA (1991).

Kendall's Advanced Theory of Statistics Volume 2A Classical Inference and the Linear Model. Stuart, Alan and J. Keith Ord and Steven Arnold. Published by Arnold, London, UK (1999).

Kendall's Advanced Theory of Statistics and Kendall's Library of Statistics

The development of modern statistical theory is reflected in the history of the late Sir Maurice Kendall's volumes, The Advanced Theory of Statistics. This landmark publication begain life as a two-colume work and grew steadily, as a single-authored work, until the late 1950s. This volume offers a major revision including a discussion of the bivariate and multivariate versions of the standard distributions and families. Other major updated include new material on skewness and kurtosis, hazard rate distributions, the bootstrap, the evaluation of the multivariate normal integral, and ratios of quadtraic forms.

Comments (4)

Iaiastta
A+++
GoodBuyMyFriends
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.
Kezan
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.
Rayli
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.

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