Elements of Computational Statistics
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Elements of Computational Statistics

 Previously published in hardcover
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Previously published in hardcover
James E. Gentle
666 g
235x155x23 mm

Preliminaries Monte Carlo Methods for Inference Randomization and Data Partitioning Bootstrap Methods Tools for Identification of Structure in Data Estimation of Functions Graphical Methods in Computational Statistics Estimation of Probability Density Functions Using Parametric Models Nonparametric Estimation of Probability Density Functions Structure in Data Statistical Models of Dependencies Appendices
This book describes techniques used in computational statistics and considers some of the areas of applications, such as density estimation and model building, in which computationally intensive methods are useful. In computational statistics, computation is viewed as an instrument of discovery; the role of the computer is not just to store data, perform computations, and produce graphs and tables, but additionally to suggest to the scientist alternative models and theories. Another characteristic of computational statistics is the computational intensity of the methods; even for datasets of medium size, high performance computers are required to perform the computations. Graphical displays and visualization methods are usually integral features of computational statistics. Will provide a more elementary introduction to these topics than other books available; Gentle is the author of two other Springer books Computationally intensive methods have become widely used both for statistical inference and for exploratory analyses of data. The methods of computational statistics involve resampling, partitioning, and multiple transformations of a dataset. They may also make use of randomly generated artificial data. Implementation of these methods often requires advanced techniques in numerical analysis, so there is a close connection between computational statistics and statistical computing. This book describes techniques used in computational statistics, and addresses some areas of application of computationally intensive methods, such as density estimation, identification of structure in data, and model building. Although methods of statistical computing are not emphasized in this book, numerical techniques for transformations, for function approximation, and for optimization are explained in the context of the statistical methods. The book includes exercises, some with solutions. The book can be used as a text or supplementary text for various courses in modern statistics at the advanced undergraduate or graduate level, and it can also be used as a reference for statisticians who use computationally-intensive methods of analysis. Although some familiarity with probability and statistics is assumed, the book reviews basic methods of inference, and so is largely self-contained. James Gentle is University Professor of Computational Statistics at George Mason University. He is a Fellow of the American Statistical Association and a member of the International Statistical Institute. He has held several national offices in the American Statistical Association and has served as associate editor for journals of the ASA as well as for other journals in statistics and computing. He is the author of Random Number Generation and Monte Carlo Methods and Numerical Linear Algebra for Statistical Applications.