## 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

Bayesian prediction | 46 |

Problems of comparison and allocation | 118 |

Perturbation analysis | 129 |

Screening tests for detecting a characteristic | 169 |

Multivariate normal prediction | 191 |

Interim analysis and sampling curtailment | 222 |

252 | |

259 | |

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### Common terms and phrases

analysis applications approach appropriate approximation assigned associated assume Bayes Bayesian calculate Chapter classification compute conditional consider continue cost decision define degrees of freedom depend determine discordancy distribution function easily effect error estimate Example exists expected experiment exponential Figure Further future Geisser given Hence independent indicate individuals inference interest interval joint Journal known likelihood linear loss function matrix maximum mean measure methods minimize model selection multivariate negative normal Note Numerical illustration observations obtain optimal parameters particular perturbation population positive possible posterior predictive density predictive distribution predictive probability prior probability problem procedure References regard region regression represents require respect result rule sample selection situation solution standard Statistical subjective sufficiently Suppose Table tion treatment unknown values variance variate XN+1 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 |