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The Statistical Analysis of Compositional Data [Paperback]

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Item description for The Statistical Analysis of Compositional Data by J. Aitchison...

This book was originally published in 1986. It is reprinted here with a new foreword, extensive postscript detailing developments in the field since publication and a selection of more recent literature references.

A recent excursion on the web in search of "compositional data" produced over 3000 entries within a great variety of disciplines. In agriculture, land use compositions; in archaeology, chemical compositions of ceramics; in developmental biology, shape analysis relating (head, trunk, leg) compositions to height; in economics, household budget patterns; in environometrics, pollutant compositions; in geology, major oxide compositions of rocks and sediment (sand, silt, clay) compositions; in literary studies, sentence compositions; in manufacturing, global car production compositions; in medicine, blood, urine and renal calculi compositions; in ornithology, plumage and artefact colour compositions of the greater bower bird and sea bird time budgets; in palaeontology, zonal pollen compositions; in psephology, US Presidential election voting proportions; in psychology and sociology, time budgets of various groups; in waste disposal studies, waste composition. There can be little doubt that appropriate statistical analysis of such compositions is a requirement of many problems in many disciplines.

This book provides a clear and systematic account of statistical methods designed to meet the special needs of the compositional data analyst. From the motivation of a number of practical examples from different disciplines and from a re-examination of the difficulties inherent in the inappropriate standard methods the author argues that any successful statistical methodology must be based on the simple perception that only the relative magnitudes of the components of a composition matter, not their absolute values.

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Item Specifications...

Pages   460
Est. Packaging Dimensions:   Length: 1" Width: 5.75" Height: 8.5"
Weight:   1.3 lbs.
Binding  Softcover
Release Date   Apr 1, 2003
Publisher   The Blackburn Press
ISBN  1930665784  
ISBN13  9781930665781  

Availability  0 units.

More About J. Aitchison

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Product Categories

1Books > Subjects > Medicine > Research > Biostatistics
2Books > Subjects > Professional & Technical > Professional Science > Biological Sciences > Biostatistics
3Books > Subjects > Science > Education > Research
4Books > Subjects > Science > Experiments, Instruments & Measurement > Methodology & Statistics
5Books > Subjects > Science > General
6Books > Subjects > Science > History & Philosophy > Science
7Books > Subjects > Science > Mathematics > Applied > Probability & Statistics

Reviews - What do customers think about The Statistical Analysis of Compositional Data?

Eduardo Dávila - Compositional Data - Plant Research  Nov 10, 2007
The data in vectors of proportions are very usuals in Agricultural Research like Plant pathology an plant physiology; such data may be: nutrient concentration in leaf, proportion of fruits with some infection process, amount of foliar area with any microbial disease, etc.
Monography of Dr. Aitchison gives an appropriate methology for the statistical analysis of compositional data that are very usuals in plant research; Aitchison's book should be in personal libraries of all people that works in Agricultural research.
Eduardo Dávila Sanabria
excellent text, very well written  Apr 14, 2006
My research involves compositional data in psychology in the scaling of preferences measured as continuous variables, e.g., proportions of time allocated to a task. I remember well the first time I encountered compositional data, dealing with some priority allocations on different policy preferences several years ago. I didn't realize the key issue at the time, namely the fact that a sum constraint on a multivariate data vector totally changes the sample space. For instance, it induces negative correlations in variables one would logically expect to be positively correlated. This can be very confusing until you realize what is going on. Doing something about it, however, is more complex. Aitcheson's book gives you the necessary tools to handle the problem. (Unfortunately software solutions are rarer and you will likely have to do some programming of your own.) This book is, as the editorial reviews indicate, the standard reference in the field. I highly recommend this book to anyone who has a decent background in mathematical statistics. You certainly need to have a solid understanding of linear algebra and transformations of multivariate distributions to make your way through it, but it is very clearly written, the math is lucid, and it has the added benefit of Aitcheson's dry British wit throughout (presuming you enjoy that sort of thing).

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