Computationally-Intensive Methods of Statistics

by James E. Gentle

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.

The outline is

• Preface
• Table of Contents
• 1. Methods of Computational Statistics
  • 1.1 Objectives in Computational Statistics
  • 1.2 Monte Carlo Methods for Inference
  • 1.3 Randomization and Data Partitioning
  • 1.4 Bootstrap Methods
  • 1.5 Dimension Reduction
  • 1.6 Graphical Methods
• 2. Data Density
  • 2.1 Estimation of Functions
  • 2.2 Estimation of Probability Density Functions
  • 2.3 Structure in Data
  • 2.4 Higher Dimensions
• 3. Statistical Models and Fitting with Data
  • 3.1 Probability Models
  • 3.2 Models of Relationships
  • 3.3 Model Inference Using Data
  • 3.4 Selection of Variables in a Model
• Bibliography
• Author Index
• Subject Index

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

James Gentle, jgentle@gmu.edu