Resampling and Computational methods
Carlo Statistical Methods
to CASt bibliographies
by Christian P. Robert & George Casella (2nd ed,
2004). Graduate-level text on modern methods in computational
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.
Introduction to the Bootstrap
by Bradley Efron and Robert. J. Tibshirani (1994).
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.
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.
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.
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.
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.
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.