Elements of Computational Statistics

by James E. Gentle

Springer, 2002

This book covers methods of computational statistics for data analysis.

I have used various parts of this material in several different courses I teach in computational statistics. The material is part of a larger work-in-progress, Computational Statistics. The larger book has extensive material on numerical analysis for statistical applications ("statistical computing"), in addition to the material on the statistical methods ("computational statistics").

The outline is

• Preface
• Table of Contents
• I. Methods of Computational Statistics
  • 1. Preliminaries
  • 2. Monte Carlo Methods for Statistical Inference
  • 3. Randomization and Data Partitioning
  • 4. Bootstrap Methods
  • 5. Tools for Identification of Structure in Data
  • 6. Estimation of Functions
  • 7. Graphical Methods in Computational Statistics
• II. Exploring Data Density and Structure
  • 8. Estimation of Probability Density Functions Using Parametric Models
  • 9. Nonparametric Estimation of Probability Density Functions
  • 10. Structure in Data
  • 11. Statistical Models of Dependencies
• Appendices
  • A. Monte Carlo Studies in Statistics
  • B. Software for Random Number Generation
• Bibliography
• Author Index
• Subject Index

Errata.

I would appreciate any feedback from readers -- corrections, suggestions, or general comments.

James Gentle, jgentle@gmu.edu