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.

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Will provide a more elementary introduction to these topics than other books available; Gentle is the author of two other Springer books

Contenu

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