Applied Bayesian Hierarchical Methods
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Applied Bayesian Hierarchical Methods

Applied Bayesian Hierarchical Methods


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About the Book

The use of Markov chain Monte Carlo (MCMC) methods for estimating hierarchical models involves complex data structures and is often described as a revolutionary development. An intermediate-level treatment of Bayesian hierarchical models and their applications, Applied Bayesian Hierarchical Methods demonstrates the advantages of a Bayesian approach

Table of Contents:
Bayesian Methods for Complex Data: Estimation and Inference. Model Fit, Comparison, and Checking. Hierarchical Estimation for Exchangeable Units: Continuous and Discrete Mixture Approaches. Structured Priors Recognizing Similarity over Time and Space. Regression Techniques using Hierarchical Priors. Bayesian Multilevel Models. Multivariate Priors, with a Focus on Factor and Structural Equation Models. Hierarchical Models for Panel Data. Survival and Event History Models. Hierarchical Methods for Nonlinear Regression. Appendix. References. Index.

About the Author :
Peter D. Congdon is a research professor of quantitative geography and health statistics in the Centre for Statistics and Department of Geography at the University of London, UK.

Review :
"...excellent ... for learning or applying [the Bayesian approach]. ... an excellent place for readers to learn and practice Bayesian concepts." -Journal of Statistical Computation and Simulation, Vol. 84, 2014 "The overall presentation of concepts is logical and supported by detailed mathematical descriptions. ... the text provides a great reference for the underlying formalities of all of the methods discussed. Throughout each chapter, the author highlights methodological issues in relation to the topics presented with references to the literature, displaying his comprehensive and up-to-date knowledge of the material. The emphasis placed on computational issues related to the implementation of MCMC routines for model fitting (with BUGS code provided at the end of each chapter) is welcome, as this issue has the potential to cause a lot of headaches for practitioners trying to employ Bayesian methods. ... a comprehensive and valuable resource." -Kris M. Jamsen and Lyle C. Gurrin, Australian and New Zealand Journal of Statistics, 2012 "... a good reference for applied work in biometrics. It makes it easy to analyze models with the same type of data structures that are described in the book by supplying the companion code." -Wolfgang Polasek, Statistical Papers, August 2012 "Many of the hierarchical modeling techniques in this book are recently proposed and new in the literature. The author provides very comprehensive references ... . Even though many examples are related to health and social science, they also would be helpful to users in engineering and other fields. ... In summary, the book presents many excellent Bayesian hierarchical modeling techniques to tackle difficult and realistic modeling issues that many researchers may encounter in their scientific areas. ... an excellent collection and reference for researchers who are interested in applying the most recent Bayesian hierarchical modeling methods to their own areas." -Zhaojun (Steven) Li, Journal of Quality Technology, Vol. 43, No. 4, October 2011


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Product Details
  • ISBN-13: 9781584887218
  • Publisher: Taylor & Francis Ltd
  • Publisher Imprint: Chapman & Hall/CRC
  • Language: English
  • No of Pages: 604
  • ISBN-10: 1584887214
  • Publisher Date: 19 May 2010
  • Binding: Digital (delivered electronically)
  • No of Pages: 604


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