## Permutation, Parametric, and Bootstrap Tests of HypothesesThis text is intended to provide a strong theoretical background in testing hypotheses and decision theory for those who will be practicing in the real worldorwhowillbeparticipatinginthetrainingofreal-worldstatisticiansand biostatisticians. In previous editions of this text, my rhetoric was somewhat tentative. I was saying, in e?ect, “Gee guys, permutation methods provide a practical real-world alternative to asymptotic parametric approximations. Why not give them a try?” But today, the theory, the software, and the hardware have come together. Distribution-free permutation procedures are the primary method for testing hypotheses. Parametric procedures and the bootstrap are to be reserved for the few situations in which they may be applicable. Four factors have forced this change: 1. Desire by workers in applied ?elds to use the most powerful statistic for their applications. Such workers may not be aware of the fundamental lemma of Neyman and Pearson, but they know that the statistic they wanttouse—acomplexscoreoraratioofscores,doesnothaveanalready well-tabulated distribution. 2. Pressure from regulatory agencies for the use of methods that yield exact signi?cance levels, not approximations. 3. A growing recognition that most real-world data are drawn from mixtures of populations. 4. A growing recognition that missing data is inevitable, balanced designs the exception. Thus, it seems natural that the theory of testing hypothesis and the more general decision theory in which it is embedded should be introduced via the permutation tests. On the other hand, certain relatively robust param- ric tests such as Student’s t continue to play an essential role in statistical practice. |

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Contents

1 | |

Optimal Procedures | 13 |

Testing Hypotheses | 33 |

Distributions 67 | 66 |

Multiple Tests | 79 |

Experimental Designs | 85 |

Multifactor Designs | 119 |

Categorical Data | 143 |

Coping with Disaster | 195 |

Exchangeability | 215 |

Increasing Computational Efficiency | 233 |

Theory of Testing Hypotheses | 255 |

Exchangeable Observations | 268 |

Bibliography | 279 |

303 | |

309 | |

### Other editions - View all

Permutation, Parametric, and Bootstrap Tests of Hypotheses Phillip I. Good No preview available - 2010 |

Permutation, Parametric, and Bootstrap Tests of Hypotheses Phillip I. Good No preview available - 2004 |

### Common terms and phrases

algorithms alternative analysis applied approximation assign asymptotic binomial bootstrap bootstrap sample cell censored Chapter chi-square compute conditional confidence interval contingency table covariates denote depend derive determined deviations estimate exact test example exchangeable exponential family factors Fisher’s identically distributed independent invariant with respect k-sample labels Lemma location parameter loss function matched pairs matrix mean measure Monte Carlo multivariate normally distributed null hypothesis number of observations obtain odds ratio order statistics original observations p-value parametric test permutation distribution permutation methods permutation test Poisson population powerful test problem procedure random variables ranks reject the hypothesis rejection region resampling sample sizes Section significance level squares sunlight Suppose symmetric synchronous rearrangements test based test statistic test the hypothesis Theorem tions transformations treatment two-sample Type I error UMPU unbiased test uniformly most powerful univariate variance vector zero