Smoothing Methods in Statistics
Edition: 2nd 1996 (Student Manual, Study Guide, etc.)
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Description: 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)
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All the information you need in one place! Each Study Brief is a summary of one specific subject; facts, figures, and explanations to help you learn faster.
Copyright year: 1996
Publication date: 5/28/1998
Size: 6.14" wide x 9.21" long x 0.37" tall
|Simple Univariate Density Estimation|
|Smoother Univariate Density Estimation|
|Multivariate Denisty Estimation|
|Smoothing Categorical Data|
|Further Applications of Smoothing|
|Descriptions of the Data Sets|
|More on Computational Issues|