
Resampling and Computational methods
Monte
Carlo Statistical Methods
by Christian P. Robert & George Casella (2nd ed,
2004). Graduatelevel text on modern methods in computational
statistics.
Includes random variable generators, Monte Carlo integration &
optimization, Markov chains & MCMC, MetropolisHastings 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, crossvalidation, hypothesis testing, adaptive
estimation, nonparametric inference.
Introduction
to Statistics Through Resampling Methods and R/SPLUS
by Phillip I. Good (2005). A short textbook with many examples using R. Includes concepts of statistics and probability, distributions, hypothesis testing, survey design, 2sample 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 SPlus 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).
Wellrespected 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. Online version available.
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