Statistical Computing: Existing Methods and Recent Developments attempts to provide a state of the art account of existing methods and recent developments in the so called new field of Statistical Computing. Fourteen different chapters deal with a wide range of topics. This includes introductory topics such as the basic numerical analysis methods, random number generation, graphical techniques used in statistical data analysis and other areas. It also covers the more specialized techniques such as the EM algorithm, genetic algorithms, nonparametric smoothing techniques, resampling methods, and artificial neural network models, to name a few. In addition, the volume also deals with the computational issues involved in the analysis of mixture models, adaptive designs, weighted distributions, and statistical signal processing, topics which are unlikely to be covered in a standard text on Statistical Computing.

Preface / Basic Numerical Analysis Techniques / Generation of Random Numbers / Graphical Methods / The EM Algorithm / Genetic Algorithms for Optimization / Regression Analysis / Nonparametric Smoothing / Analysis Time Series Data / A Gentle Introduction to Resampling Plans / Neural Network Models for Regression and Classification / Statistical Computing in Mixture Models / Perspectives in Adaptive Allocation Designs: Computational Issues / Weighted Distributions / Computational Aspects in Statistical Signal Processing

Senior Undergraduate and Graduate Students and Teachers