Smoothing Methods in StatisticsSpringer Science & Business Media, 6 Jun 1996 - 338 halaman This book surveys the uses of smoothing methods in statistics. The coverage has an applied focus, and is very broad, including simple and complex univariate and multivariate density estimation, nonparametric regression estimation, categorical data smoothing, and applications of smoothing to other areas of statistics. The book will be of particular interest to data analysts, as arguments generally proceed from actual data rather than statistical theory. The "Background Material" sections will interest statisticians studying the area of smoothing methods. The list of over 750 references allows researchers to find the original sources for more details. The "Computational Issues" sections provide sources for statistical software that implements the discussed methods, including both commercial and non-commercial sources. The book can also be used as a textbook for a course in smoothing. Each chapter includes exercises with a heavily computational focus based upon the data sets used in the book. "It is an excellent reference to the field and has no rival in terms of accessibility, coverage, and utility."(Journal of the American Statistical Association) "This book provides an excellent overview of smoothing methods and concepts, presenting material in an intuitive manner with many interesting graphics...This book provides a handy reference for practicing statisticians and other data analysts. In addition, it is well organized as a classroom textbook." (Technometrics) |
Isi
Introduction | 1 |
Simple Univariate Density Estimation | 13 |
Smoother Univariate Density Estimation | 40 |
Multivariate Density Estimation | 96 |
Nonparametric Regression | 134 |
Smoothing Ordered Categorical Data | 215 |
Further Applications of Smoothing | 252 |
Appendices | 275 |
References | 290 |
321 | |
329 | |
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Istilah dan frasa umum
additive American analysis Annals of Statistics apparent approach approximate Association asymptotic bandwidth bias bins bootstrap boundary calculate cell Chapter choice choosing compared Computational conditional construction correction corresponding cross-validation cubic curve defined density estimation derivatives described discriminant discussed distance distribution effect error examined example Exercise Figure fixed frequency polygon function Gaussian given gives Hall higher histogram improved increase Jones Journal kernel estimator kernel regression least likelihood linear estimate Marron mean measure methods minimizer MISE modes multivariate nearest normal noted observations optimal parameter pattern plot plug-in polynomial positive possible probability problem projection properties proposed provides quadratic regions rule salary sample scale Scott selectors showed similar smoothing smoothing spline spline squared Statistics structure suggests taking term tests Theory transformation true values variable variance varying weighted width zero
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