Written to convey an intuitive feel for both theory and practice, its main objective is to illustrate what a powerful tool density estimation can be when used not only with univariate and bivariate data but also in the higher dimensions of trivariate and quadrivariate information. Major concepts are presented in the context of a histogram in order to simplify the treatment of advanced estimators. Features 12 four-color plates, numerous graphic illustrations as well as a multitude of problems and solutions.
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Written to convey an intuitive feel for both theory and practice, its main objective is to illustrate what a powerful tool density estimation can be when used not only with univariate and bivariate data but also in the higher dimensions of trivariate and quadrivariate information. Major concepts are presented in the context of a histogram in order to simplify the treatment of advanced estimators. Features 12 four-color plates, numerous graphic illustrations as well as a multitude of problems and solutions.
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33 of 33 people found the following review helpful:
5.0 out of 5 stars
about the only practical book on the topic,
By
This review is from: Multivariate Density Estimation: Theory, Practice, and Visualization (Wiley Series in Probability and Statistics) (Hardcover)
There are a number of good books dealing with univariate density estimates. Some are very theoretical and some are very practical. My favorite is the one by Silverman because it provides intuition for the kernel density estimation methods and gets to the key point. David Scott has produced a volume that is somewhat longer than Silverman's and is the only book I have found for estimating multivariate densities. He writes very clearly on a difficult topic and provides good graphical information.
12 of 12 people found the following review helpful:
4.0 out of 5 stars
Excellent treatment of the histogram,
By A Customer
This review is from: Multivariate Density Estimation: Theory, Practice, and Visualization (Wiley Series in Probability and Statistics) (Hardcover)
This self-contained title represents a thorough treatment of both univariate and multivariate density estimation, with an emphasis on multivariate applications not found in other texts. A valuable, and perhaps unique, feature of this book is its clear discussion of the theory of kernel density estimation in terms of the conventional histogram, the latter being quite intuitive to most students.The author uses applied mathematics, expected value operations, and asymptotic theory to draw general conclusions about the relative performance between different density estimation techniques. The purpose is to provide estimators that are "optimal" for higher dimensioned problems, since the computational burden grows exponentially with higher dimensions (thus making exploratory data analysis more expensive). With this in mind, the author is justified to show how the thorough treatment of theory relates to practice and visualization. Ideas are reinforced by many problems at the end of each chapter (I regret that the answers to these problems are not included, or would have otherwise preferred just a few more examples). One nice feature is the author puts boxes around the most important theorems and results, making them easier to identify for reference purposes later. There is an emphasis on the way univariate concepts extend to lower dimensional multivariate problems, so that only a minority of the text might be considered exclusively multivariate in scope. The book contains a valuable list of references and a terse but useful appendix on 3-D computer visualization. As a result, this text educates on more than density estimation, but does so assuming a fairly sophisticated level of audience education and background beyond the first few chapters. Since most of the terminology and basic concepts are defined early on, yet the most interesting applications and methods are discussed toward the end, this title is not easily skimmed. I have found this book cited often by other texts in this fields.
1 of 2 people found the following review helpful:
5.0 out of 5 stars
essential,
By hypergeometric "hypergeometric" (Westwood, MA USA) - See all my reviews
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This review is from: Multivariate Density Estimation: Theory, Practice, and Visualization (Wiley Series in Probability and Statistics) (Hardcover)
This has become of four texts I am constantly consulting.The others? Rice, MATHEMATICAL STATISTICS AND DATA ANALYSIS, 3rd edition. Bivand, Pebesma, Gomez-Rubio, APPLIED SPATIAL DATA ANALYSIS WITH R Ramsey, Schafer, THE STATISTICAL SLEUTH: A COURSE IN METHODS OF DATA ANLYSIS
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Inside This Book
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Key Phrases - Statistically Improbable Phrases (SIPs):
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oversmoothed bandwidth, pairwise scatter diagrams, biweight kernel, bin origin, fixed kernel estimator, pointwise bias, optimal bin widths, geyser data, gas flow data, integrated squared bias, snowfall data, optimal smoothing parameters, boundary kernels, multivariate histograms, shifted histograms, integrated variance, equivalent sample sizes, equivalent kernel, pairwise scatterplots, kernel estimate, bin counts, bump hunting, linear blend, square bins, frequency polygon Key Phrases - Capitalized Phrases (CAPs): (learn more)
Old Faithful, Color Plate, Comparing Equations, Verify Equation New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
oversmoothed bandwidth, pairwise scatter diagrams, biweight kernel, bin origin, fixed kernel estimator, pointwise bias, optimal bin widths, geyser data, gas flow data, integrated squared bias, snowfall data, optimal smoothing parameters, boundary kernels, multivariate histograms, shifted histograms, integrated variance, equivalent sample sizes, equivalent kernel, pairwise scatterplots, kernel estimate, bin counts, bump hunting, linear blend, square bins, frequency polygon Key Phrases - Capitalized Phrases (CAPs): (learn more)
Old Faithful, Color Plate, Comparing Equations, Verify Equation 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 100
books:
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100
books
cite this book:
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- Data Analysis (New Topics for Secondary School Mathematics) by Nc School of Sci and Math in Back Matter (1), Back Matter (2), and Back Matter (3)
- Computer Graphics by Acm Press in Back Matter (1), and Back Matter (2)
- Statistical Analysis of Finite Mixture Distributions (Wiley Series in Probability and Statistics - Applied Probability and Statistics Section) by D. M. Titterington in Back Matter (1), and Back Matter (2)
- Linear Models: Least Squares and Alternatives (Springer Series in Statistics) by C.R. Rao in Back Matter (1), and Back Matter (2)
- Understanding Robust and Exploratory Data Analysis by David C. Hoaglin in Back Matter (1), and Back Matter (2)
See all 100 books this book cites
- Smoothing and Regression: Approaches, Computation, and Application by Michael G. Schimek on page 391, page 461, and Back Matter
- A Probabilistic Analysis of the Sacco and Vanzetti Evidence (Wiley Series in Probability and Statistics) by Joseph B. Kadane in Back Matter
- Statistical Methods for Reliability Data (Wiley Series in Probability and Statistics) by William Q. Meeker in Back Matter
- Generalized, Linear, and Mixed Models (Wiley Series in Probability and Statistics) by Charles E. McCulloch in Back Matter
- Statistical Size Distributions in Economics and Actuarial Sciences (Wiley Series in Probability and Statistics) by Christian Kleiber in Back Matter
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