Elements of computational statistics

Statistics and computing

James E. Gentle

440 pages, parution le 16/10/2002

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Résumé

Computationally intensive methods have become widely used both for statistical inference and for exploratory analyses of data. The methods of computational statistics involve resempling, 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. ContentsI- Methods of Computational Statistics

Preliminaries
Monte Carlo Methods for Statistical Inference
Randomization and Data Partitioning
Bootstrap Methods
Tools for Identification of Structure in Data
Estimation of Functions
Graphical Methods in Computational Statistics

II- Exploring Data Density and Structure

Estimation of Probability Density Functions Using Parametric Models
Nonparametric Estimation of Probability Density Functions
Structure in Data
Statistical Models of Dependencies

L'auteur - James E. Gentle

James Gentle is University Professor of Computational Statistics at George Mason University. He is a fellow of the American Statistical Association (ASA)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 Applications in Statistics.

Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	James E. Gentle
Parution	16/10/2002
Nb. de pages	440
Format	16 x 24
Couverture	Relié
Poids	730g
Intérieur	Noir et Blanc
EAN13	9780387954899
ISBN13	978-0-387-95489-9