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CASt online resources

Resampling and Computational methods
Monte Carlo Statistical Methods
    by Christian P. Robert & George Casella (2nd ed, 2004). Graduate-level text on modern methods in computational statistics. Includes random variable generators, Monte Carlo integration & optimization, Markov chains & MCMC, Metropolis-Hastings algorithm, slice sampler, Gibbs samplers, reversible jump algorithms, convergence, perfect & importance sampling.

An Introduction to the Bootstrap
    by Bradley Efron and Robert. J. Tibshirani (1994).  Widely used textbook on bootstrap and related resampling methods.  Covers bootstrap confidence intervals, regression models, jackknife & permutation, bias, cross-validation, hypothesis testing, adaptive estimation, nonparametric inference.

Introduction to Statistics Through Resampling Methods and R/S-PLUS
    by Phillip I. Good (2005). A short textbook with many examples using R. Includes concepts of statistics and probability, distributions, hypothesis testing, survey design, 2-sample tests, categorical data, multivariate analysis, regression types, classification, and summarizing results.

Bootstrap Methods: A Practitioner's Guide
    by Michael R. Chernick (1999).  A concise introduction to bootstrap resampling procedures for estimation, confidence sets, linear and nonlinear regression, forecasting and time series, and other problems (kriging, censoring, point processes, missing data).  Comparison of related methods such as jackknife and Bayesian bootstrap.

Bootstrap Methods and Their Applications
    by A. C. Davison & D. V. Hinkley (1997). Useful presentation with examples from S-Plus including: bootstrap methods, tests and confidence intervals, linear regression and more complex dependencies, and semiparametric likelihood inference.

Elements of Statistical Learning: Data Mining, Inference, and Prediction
    by Trevor Hastie, Robert Tibishrani & Jerome Friedman (2001).  Well-respected undergraduate text in computationally intensive methods of data mining and machine learning emphasizing concepts rather than applications.  Topics include neural networks, support vector machines, classification trees, boosting & related methods.

Numerical Analysis for Statisticians
    by Kenneth Lange (1999). Graduate level text covering a wide range of methods including least squares, resampling & the bootstrap, Markov chain Monte Carlo, Fourier series & wavelets, and the EM Algorithm.  On-line version available.
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