This is the first comprehensive, yet clearly presented, account of statistical methods for analysing spherical data. The analysis of data, in the form of directions in space or of positions of points on a spherical surface, is required in many contexts in the earth sciences, astrophysics and other fields, yet the methodology required is disseminated throughout the literature. Statistical Analysis of Spherical Data aims to present a unified and up-to-date account of these methods for practical use. The emphasis is on applications rather than theory, with the statistical methods being illustrated throughout the book by data examples.
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'The book is up-to-date and very comprehensive, yet carefully written, with excellent index and bibliography. Notable features are the worked examples using real data and the profusion of computer-generated plots and diagrams. Undoubtedly, this will become the standard reference work on the application of statistical methods to spherical data.' R. M. Clark, The Australian Journal of Statistics
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1 of 1 people found the following review helpful:
5.0 out of 5 stars
Great handbook on directional statistics,
By Pavel Doubrovine "pavel.doubrovine" (Oslo, Norway) - See all my reviews
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This review is from: Statistical Analysis of Spherical Data (Paperback)
I you are just looking for a cookbook with ready to use recipes for the statistical analysis of directional data - this book is for you. It gives necessary rudiments of the relevant theory, but doesn't dive deeply into it. What it presents is algorithm-type descriptions covering various statistical test and parameter estimation techniques applied to directions (unit vectors or points on the sphere) and undirected data (axes). The use of these techniques is illustrated are with abundant and well described real life examples. The range of applications is extensive and covers virtually every single situation that could arise in routine work of a paleomagnetist, structural geologist, or any other Earth scientist using directional data. (Being an Earth scientist myself I cannot really testify how well it covers the aspect relevant to other fields, but I believe it is quite comprehensive.) These include
-- Simple exploratory and graphical techniques aimed to help in identifying an appropriate statistical model for the description of a specific sample of spherical data -- Formal tests to see whether the data conform to a distribution of specific type (e.g., uniform, Fisher, Watson, Kent, Wood distributions) -- General tests for uniformity and rotational symmetry -- Simple non-parametric estimation of sample measures (e.g., mean, median, their confidence regions, etc.) -- Parameter estimation for parametric models -- Testing whether the sample could be drawn from a distribution with a specific properties (e.g, known mean, know dispersion) -- Identifying outliers and formal tests for discordancy -- Comparing two (or more) samples -- whether they could be drawn form the distribution, from distinct distributions with a common mean, common median, common concentration parameter, etc. -- Estimation of common measures from two or more samples -- Rudiments of correlation and regression analysis -- Rudiments of bootstrap and other re-sampling techniques What the book is missing entirely is the statistical treatment of data in plate tectonic reconstruction (estimation of tectonic rotations, their uncertainties, quality of fit etc.) This is not surprising because the book was written in 1987, when the relevant theory is only began to develop. For a more mathematically minded and those who are interested in the theory that lies beneath the techniques described in this book, I would recommend the "Directional Statistics" by Mardia and Jupp (1999, Whiley), which is also quite a pleasant read.
0 of 3 people found the following review helpful:
4.0 out of 5 stars
Statistical analysis of spherical data,
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This review is from: Statistical Analysis of Spherical Data (Paperback)
This book is a very good book which treated spherical data comprehensively.
Although written very carefully, if a fundamental description is added to a slight degree, the buyer of this book will increase more. Thank you for a useful book.
0 of 14 people found the following review helpful:
5.0 out of 5 stars
The best to dream,
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This review is from: Statistical Analysis of Spherical Data (Paperback)
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Inside This Book
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First Sentence:
Many different ways of representing a three-dimensional unit vector or axis have been developed over the centuries, due not only to the requirements of disciplines (Astronomy, Geodesy, Geology, Geophysics, Mathematics,...) but also to diverse needs within a discipline: in Geology, for example, there appear to be five or six systems in current use. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
elliptical confidence cone, colatitude plot, sample principal axis, main data mass, common mean direction, plunge azimuth, common principal axis, sample mean direction, unknown mean direction, girdle distribution, girdle model, normalised eigenvalues, spherical random variable, uniform probability plot, mean resultant length, median direction, spherical data, axial data, vectorial data, girdle case, suitable model for the data, sunflower plots, confidence cones, rotationally symmetric distribution, unimodal data Key Phrases - Capitalized Phrases (CAPs): (learn more)
Continuing Example, Right Ascension, Calculate the Kolmogorov-Smirnov, Introduction There New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Many different ways of representing a three-dimensional unit vector or axis have been developed over the centuries, due not only to the requirements of disciplines (Astronomy, Geodesy, Geology, Geophysics, Mathematics,...) but also to diverse needs within a discipline: in Geology, for example, there appear to be five or six systems in current use. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
elliptical confidence cone, colatitude plot, sample principal axis, main data mass, common mean direction, plunge azimuth, common principal axis, sample mean direction, unknown mean direction, girdle distribution, girdle model, normalised eigenvalues, spherical random variable, uniform probability plot, mean resultant length, median direction, spherical data, axial data, vectorial data, girdle case, suitable model for the data, sunflower plots, confidence cones, rotationally symmetric distribution, unimodal data Key Phrases - Capitalized Phrases (CAPs): (learn more)
Continuing Example, Right Ascension, Calculate the Kolmogorov-Smirnov, Introduction There New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 20
books:
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26
books
cite this book:
See all 26 books citing this book
- Directional Statistics by Kanti V. Mardia in Front Matter, and Back Matter
- Sampling Techniques, 3rd Edition by William G. Cochran in Back Matter
- Spherical Astronomy by Robin M. Green in Back Matter
- Mineralogy: Concepts, Descriptions, Determinations by L. G. Berry in Back Matter
- Paleocurrents and Basin Analysis by Paul Edwin Potter in Back Matter
See all 20 books this book cites
- Computational Geosciences with Mathematica by William C. Haneberg on page 55, and page 171
- Categorical and Directional Data, Volume 2, Spatial Data Analysis by Example by Graham J. G. Upton on page 10, and Back Matter
- Directional Statistics by Kanti V. Mardia in Front Matter, and Back Matter
- Introduction to Color Imaging Science by Hsien-Che Lee in Back Matter
- Multivariate Analysis: Part 2: Classification, Covariance Structures and Repeated Measurements (Pt. 2) by W. J. Krzanowski in Back Matter
See all 26 books citing this book
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