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Bayesian Thinking, Modeling and Computation, Vol 25. Handbook of Statistics

  • ID: 1762700
  • Book
  • November 2005
  • 1062 Pages
  • Elsevier Science and Technology
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This volume describes how to develop Bayesian thinking, modelling and computation both from philosophical, methodological and application point of view. It further describes parametric and nonparametric Bayesian methods for modelling and how to use modern computational methods to summarize inferences using simulation. The book covers wide range of topics including objective and subjective Bayesian inferences with a variety of applications in modelling categorical, survival, spatial, spatiotemporal, Epidemiological, software reliability, small area and micro array data. The book concludes with a chapter on how to teach Bayesian thoughts to nonstatisticians.

- Critical thinking on causal effects- Objective Bayesian philosophy- Nonparametric Bayesian methodology- Simulation based computing techniques- Bioinformatics and Biostatistics

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1. Bayesian Inference for Casual Effects (Donald B. Rubin)
2. Reference Analysis (Josè M. Bernardo)
3. Probability Matching Priors (Gauri Sankar Datta and Trevor J. Sweeting)
4. Model Selection and Hypothesis Testing Based on Objective Probabilities and Bayes Factors (Luis Raul Pericchi)
5. Role of P-values and other measures of evidence in Bayesian Analysis (Jayanta Ghosh, Sumitra Purkayastha and Tapas Samanta)
6. Bayesian Model Checking and Model Diagnostics (Hal S. Stern and Sandip Sinharay)
7. The Elimination of Nuisance Parameters (Brunero Liseo)
8. Bayesian Estimation of Multivariate Location Parameters (Ann Cohen Brandwein and William E. Strawdermann)
9. Bayesian Nonparametric Modeling and Data Analysis: An Introduction (Timothy E. Hanson, Adam J. Branscum and Wesley O. Johnson)
10. Some Bayesian Nonparametric Models (Paul Damien)
11. Bayesian Modeling in the Wavelet Domain (Fabrizio Ruggeri and Brani Vidakovic)
12. Bayesian Nonparametric Inference (Stephen Walker)
13. Bayesian Methods for Function Estimation (Nidhan Choudhuri, Subhashis Ghosal and Anindya Roy)
14. MCMC Methods to Estimate Bayesian Parametric Models (Antonietta Mira)
15. Bayesian Computation: From Posterior Densities to Bayes Factors, Marginal Likelihoods, and Posterior Model Probabilities (Ming-Hui Chen)
16. Bayesian Modelling and Inference on Mixtures of Distributions (Jean-Michel Marin, Kerrie Mengersen and Christian P. Robert)
17. Simulation Based Optimal Design (Peter Müller)
18. Variable Selection and Covariance Selection in Multivariate Regression Models (Edward Cripps, Chris Carter and Robert Kohn)
19. Dynamic Models (Helio S. Mignon, Dani Gamerman, Hedibert F. Lopes and Marco A.R. Ferreira)
20. Bayesian Thinking in Spatial Statistics (Lance A. Waller)
21. Robust Bayesian Analysis (Fabrizio Ruggeri, David Ríos Insua and Jacinto Martín)
22. Elliptical Measurement Error Models
A Bayesian Approach (Heleno Bolfarine and R.B. Arellano-Valle)
23. Bayesian Sensitivity Analysis in Skew-elliptical Models (Ignacio Vidal, Pilar Iglesias and Marcia Branco)
24. Bayesian Methods for DNA Microarray Data Analysis (Veerabhadran Baladandyuthapani, Shubhankar Ray and Bani Mallick)
25. Bayesian Biostatistics (David B. Dunson)
26. Innovative Bayesian Methods for Biostatistics and Epidemiology (Paul Gustafson, Shahadut Hossain and Lawrence McCandless)
27. Bayesian Analysis of Case-Control Studies (Bhramar Mukherjee, Samiran Sinha and Malay Ghosh)
28. Bayesian Analysis of ROC Data (Valen E. Johnson and Timothy D. Johnson)
29. Modeling and Analysis for Categorical Response Data (Siddhartha Chib)
30. Bayesian Methods and Simulation-Based Computation for Contingency Tables (James H. Albert)
31. Multiple Events Time Data: A Bayesian Recourse (Debajyoti Sinha and Sujit K. Ghosh)
32. Bayesian Survival Analysis for Discrete Data with Left-Truncation and Interval Censoring (Chong Z. He and Dongchu Sun)
33. Software Reliability (Lynn Kuo)
34. Bayesian Aspects of Small Area Estimation (Tapabrata Maiti)
35. Teaching Bayesian Thought to Nonstatisticians (Dalene K. Stangl)
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Dey, Dipak K.
Rao, C.R.
C. R. Rao, born in India is one of this century's foremost statisticians, received his education in statistics at the Indian Statistical Institute (ISI), Calcutta. Rao is currently at Penn State as Eberly Professor of Statistics and Director of the Center for Multivariate Analysis. His research has influenced not only statistics, but also the physical, social and natural sciences and engineering.

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