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Parallel Numerical Algorithms (T. L. Freeman and C. Phillips)

listed at the end of each chapter, for theoretical aspects. It covers virtually all well-known applications of the bootstrap method and much more. Most ofthe numerous examples given to illustrate the methodologies use real data sets. The topics covered range from the standard applications such as estimation of standard errors, regression model, and confidence intervals to such topics as approximate likelihoods and bioequivalence. Other topics include permutations tests, cross validation estimates of prediction error, and comparison with bootstrap estimates, adaptive estimates, jackknife approximations and estimates of bias using jackknife, geometric representation ofbootstrap andjackknife, and efficient bootstrap computations. The book is very nicely written and organized, and is enjoyable to read. There are twenty-six chapters. Each of the first twenty-five chapters ends with bibliographic notes and a set of problems. The last chapter has a discussion. Finally, there is an appendix containing a description of the computer program used in the book. This book certainly is a very valuable addition to the literature on bootstrap, and the authors ought to be commended for the excellent work they have done. The book can be easily adapted either for a one semester or two semester courses, as suggested by the authors.