Focussing on applications, this book covers a very broad range, including simple and complex univariate and multivariate density estimation, nonparametric regression estimation, categorical data smoothing, and applications of smoothing to other areas of statistics. It will thus be of particular interest to data analysts, as arguments generally proceed from actual data rather than statistical theory, while the "Background Material" sections will interest statisticians studying the field. Over 750 references allow researchers to find the original sources for more details, and the "Computational Issues" sections provide sources for statistical software that use the methods discussed. Each chapter includes exercises with a heavily computational focus based upon the data sets used in the book, making it equally suitable as a textbook for a course in smoothing.
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"...an excellent reference to the field and has no rival in terms of accessibility, coverage, and utility." (Jnl. of the Am. Statistical Association)
"...an excellent overview of smoothing methods and concepts, presenting material in an intuitive manner with many interesting graphics." (Technometrics)
"...an excellent overview of smoothing methods and concepts, presenting material in an intuitive manner with many interesting graphics." (Technometrics)
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3 of 5 people found the following review helpful:
4.0 out of 5 stars
Very good book,
This review is from: Smoothing Methods in Statistics (Springer Series in Statistics) (Hardcover)
This book is very good. The motivation for the issues on nonparametric methods a excellent. The only reason why I haven't given it 5 stars is that I would like to see more precise proofs.
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Inside This Book
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Key Phrases - Statistically Improbable Phrases (SIPs):
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nonparametric regression, kernel density estimation, spline smoothing, mine accident data, boundary kernel estimate, local quadratic estimate, simple density estimators, order kernel estimator, univariate density estimation, local linear estimate, bank note data, locally varying bandwidth, quadratic loess, boundary bias correction, additive model estimate, local likelihood estimator, newspaper circulation data, vineyard data, electricity usage data, calcium carbonate data, smoothed counts, variable kernel estimate, variable kernel estimator, sparse asymptotics, cubic estimate Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Smoother Univariate Density Estimation, Long Island, Multivariate Density Estimation, Smoothing Ordered Categorical Data, Simple Univariate Density Estimation, World Wide Web, Taylor Series, Days Fig, Old Faithful Geyser, Exercises Exercise, Further Applications of Smoothing, Row Fig, Skeena River, Consider Fig, Cell Fig, Smoothing Sparse Contingency Tables, Year Fig, Smoothing Sparse Multinomials, Fitted Gaussian, Simple Density Estimation Methods, Parametric Compromise, Other Estimators, National Hockey League, New Guinea New!
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nonparametric regression, kernel density estimation, spline smoothing, mine accident data, boundary kernel estimate, local quadratic estimate, simple density estimators, order kernel estimator, univariate density estimation, local linear estimate, bank note data, locally varying bandwidth, quadratic loess, boundary bias correction, additive model estimate, local likelihood estimator, newspaper circulation data, vineyard data, electricity usage data, calcium carbonate data, smoothed counts, variable kernel estimate, variable kernel estimator, sparse asymptotics, cubic estimate Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Smoother Univariate Density Estimation, Long Island, Multivariate Density Estimation, Smoothing Ordered Categorical Data, Simple Univariate Density Estimation, World Wide Web, Taylor Series, Days Fig, Old Faithful Geyser, Exercises Exercise, Further Applications of Smoothing, Row Fig, Skeena River, Consider Fig, Cell Fig, Smoothing Sparse Contingency Tables, Year Fig, Smoothing Sparse Multinomials, Fitted Gaussian, Simple Density Estimation Methods, Parametric Compromise, Other Estimators, National Hockey League, New Guinea New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
99
books
cite this book:
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- Introduction to Nonparametric Regression (Wiley Series in Probability and Statistics) by K. Takezawa on page 318, page 405, and Back Matter
- Functional Data Analysis (Springer Series in Statistics) by Jim Ramsay in Back Matter (1), and Back Matter (2)
- Analyzing Categorical Data (Springer Texts in Statistics) by Jeffrey S. Simonoff in Back Matter (1), and Back Matter (2)
- Economics Essays: A Festschrift for Werner Hildenbrand by Gerard Debreu on page 274
- Regression Graphics: Ideas for Studying Regressions Through Graphics by R. Dennis Cook in Back Matter
See all 99 books citing this book
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