Statistical Inference: An Integrated Bayesian/Likelihood Approach (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)

Statistical Inference: An Integrated Bayesian/Likelihood Approach (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)

By: Murray Aitkin (author)Hardback

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Filling a gap in current Bayesian theory, Statistical Inference: An Integrated Bayesian/Likelihood Approach presents a unified Bayesian treatment of parameter inference and model comparisons that can be used with simple diffuse prior specifications. This novel approach provides new solutions to difficult model comparison problems and offers direct Bayesian counterparts of frequentist t-tests and other standard statistical methods for hypothesis testing. After an overview of the competing theories of statistical inference, the book introduces the Bayes/likelihood approach used throughout. It presents Bayesian versions of one- and two-sample t-tests, along with the corresponding normal variance tests. The author then thoroughly discusses the use of the multinomial model and noninformative Dirichlet priors in "model-free" or nonparametric Bayesian survey analysis, before covering normal regression and analysis of variance. In the chapter on binomial and multinomial data, he gives alternatives, based on Bayesian analyses, to current frequentist nonparametric methods. The text concludes with new goodness-of-fit methods for assessing parametric models and a discussion of two-level variance component models and finite mixtures. Emphasizing the principles of Bayesian inference and Bayesian model comparison, this book develops a unique methodology for solving challenging inference problems. It also includes a concise review of the various approaches to inference.

About Author

Murray Aitkin is an honorary professorial fellow in the Department of Mathematics and Statistics at the University of Melbourne in Australia.


Theories of Statistical Inference Example Statistical models The likelihood function Theories Nonmodel-based repeated sampling Conclusion The Integrated Bayes/Likelihood Approach Introduction Probability Prior ignorance The importance of parametrization The simple/simple hypothesis testing problem The simple/composite hypothesis testing problem Posterior likelihood approach Bayes factors The comparison of unrelated models Example-GHQ score and psychiatric diagnosis t-Tests and Normal Variance Tests One-sample t-test Two samples: equal variances The two-sample test Two samples: different variances The normal model variance Variance heterogeneity test Unified Analysis of Finite Populations Sample selection indicators The Bayesian bootstrap Sampling without replacement Regression models More general regression models The multinomial model for multiple populations Complex sample designs A complex example Discussion Regression and Analysis of Variance Multiple regression Nonnested models Binomial and Multinomial Data Single binomial samples Single multinomial samples Two-way tables for correlated proportions Multiple binomial samples Two-way tables for categorical responses-no fixed margins Two-way tables for categorical responses-one fixed margin Multinomial "nonparametric" analysis Goodness of Fit and Model Diagnostics Frequentist model diagnostics Bayesian model diagnostics The posterior predictive distribution Multinomial deviance computation Model comparison through posterior deviances Examples Simulation study Discussion Complex Models The data augmentation algorithm Two-level variance component models Test for a zero variance component Finite mixtures References Author Index Subject Index

Product Details

  • ISBN13: 9781420093438
  • Format: Hardback
  • Number Of Pages: 254
  • ID: 9781420093438
  • weight: 567
  • ISBN10: 1420093436

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