## Predictive InferenceThe author's research has been directed towards inference involving observables rather than parameters. In this book, he brings together his views on predictive or observable inference and its advantages over parametric inference. While the book discusses a variety of approaches to prediction including those based on parametric, nonparametric, and nonstochastic statistical models, it is devoted mainly to predictive applications of the Bayesian approach. It not only substitutes predictive analyses for parametric analyses, but it also presents predictive analyses that have no real parametric analogues. It demonstrates that predictive inference can be a critical component of even strict parametric inference when dealing with interim analyses. This approach to predictive inference will be of interest to statisticians, psychologists, econometricians, and sociologists. |

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### Contents

Preface | 1 |

Bayesian prediction | 46 |

Problems of comparison and allocation | 118 |

Perturbation analysis | 129 |

Screening tests for detecting a characteristic | 169 |

Mullivariate normal prediction | 191 |

Interim analysis and sampling curtailment | 222 |

252 | |

259 | |

### Common terms and phrases

Aitehison American Statistical Association approximation assume Bayes Bayes theorem Bayesian approach Bayesian inference calculate censored classification compute conditional predictive conjugate prior consider covariance covariance matrix data translated decision rules degrees of freedom depend diagnostic discordancy distribution function easily estimate exponential F variate frequentist Further future observations future values Geisser given Hellinger distances Hence hyperparameters illustration of Example improper prior independent inference interim analysis interval Journal known Kullback linear loss function matrix maximizing maximum likelihood methods minimize MLPD model selection multivariate normal negative normal distribution Note Numerical illustration obtain optimal parameters particular perturbation population posterior distribution posterior probability predicted value predictive density predictive distribution predictive probability function prior density procedure random sample random variable region regression result Royal Statistical Society sample reuse screening sequential situation solution standard Suppose Table theorem tion training sample unknown variance vector xN+l yields

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### References to this book

Generalized Linear Models: A Bayesian Perspective Dipak K. Dey,Sujit K. Ghosh,Bani K. Mallick Limited preview - 2000 |