Case Studies in Bayesian Statistics

1. Front Matter

   Pages N2-xiv

   PDF

2. Invited Papers

   1. Front Matter

      Pages 1-1

      PDF

   2. Statistical Models for Biological Sequence Motif Discovery

      • Jin S. Liu, Mayetri Gupta, Xiaole Liu, Linda Mayerhofere, Charles E. Lawrence

      Pages 3-32

   3. Multi-Course Treatment Strategies for Clinical Trials of Rapidly Fatal Diseases

      • Peter F. Thall, Hsi-Guang Sung, Elihu H. Estey

      Pages 33-89

   4. Recognition of Faces versus Greebles: A Case Study in Model Selection

      • Kert Viele, Robert E. Kass, Michael J. Tarr, Marlene Behrmann, Isabel Gauthier

      Pages 91-133

3. Contributed Papers

   1. Front Matter

      Pages 135-135

      PDF

   2. Spatial Semiparametric Proportional Hazards Models for Analyzing Infant Mortality Rates in Minnesota Counties

      • Sudipto Banerjee, Bradley P. Carlin

      Pages 137-151

   3. Bayes and asymptotically pointwise optimal stopping rules for the detection of influenza epidemics

      • Michael I. Barón

      Pages 153-163

   4. Exploring Space-Time Structure in Ozone Concentration Using a Dynamic Process Convolution Model

      • Catherine A. Calder, Christopher Holloman, David Higdon

      Pages 165-176

   5. Correlations Between Clinical Mastitis in Different Periods of First-Lactation Norwegian Cattle Using a Multivariate Threshold Model

      • Yu-mei Chang, Daniel Gianola, Bjørg Heringstad, Gunnar Klemetsdal

      Pages 177-192

   6. Working Memory Impairments in Schizophrenia Patients: A Bayesian Bivariate IRT Analysis

      • Samantha Cook, John Barnard, Yungtai Lo, Donald B. Rubin, Michael J. Coleman, Steven Matthysse et al.

      Pages 193-206

   7. Improving MCMC Mixing for a GLMM Describing Pathogen Concentrations in Water Supplies

      • Ciprian M. Crainiceanu, David Ruppert, Jery R. Stedinger, Christopher T. Behr

      Pages 207-221

   8. Bayesian Analysis of a Population HIV Dynamic Model

      • Cong Han, Kathryn Chaloner, Alan S. Perelson

      Pages 223-237

   9. Hierarchical Bayes Models for Relating Particulate Matter Exposure Measures

      • Murali Haran, Bradley P. Carlin, John L. Adgate, Gurumurthy Ramachandran, Lance A. Waller, Alan E. Gelfand

      Pages 239-254

   10. A Bayesian Approach to Reducing Heterogeneity in Laboratory Measures: An Illustration from Schizophrenia Research

       • Shane T. Jensen, Mark F. Lenzenweger, Donald B. Rubin

       Pages 255-266

   11. Evaluating the Impact of Environmental Stressors on Benthic Microinvertebrate Communities via Bayesian Model Averaging

       • Ilya A. Lipkovich, Eric P. Smith, Keying Ye

       Pages 267-283

   12. Comovements and Contagion in Emergent Markets: Stock Indexes Volatilities

       • Hedibert Freitas Lopes, Helio S. Migon

       Pages 285-300

   13. Some Models for Down’s Syndrome Risk Prediction

       • J. M. Marin, R. Montes Diez, D. Rios Insua

       Pages 301-310

   14. Multi-scale Relationships Between Coarse Woody Debris and Presence/Absence of Western Hemlock in the Oregon Coast Range

       • Vicente J. Monleon, Alix I. Gitelman, Andrew N. Gray

       Pages 311-318

   15. Was it a car or a cat I saw? An Analysis of Response Times for Word Recognition

       • Mario Peruggia, Trisha Van Zandt, Meng Chen

       Pages 319-334

The 6th Workshop on Case Studies in Bayesian Statistics was held at the Carnegie Mellon University in October, 2001. This volume contains the invited case studies with the accompanying discussion as well as contributed papers selected by a refereeing process.

• Statistica
• Volume
• bayesian statistics
• cognition
• model
• selection
• statistics
• university

From the reviews:

TECHNOMETRICS

"…well written and can be appreciated by those with little biology background…In general, I found many of the papers collected in this book interesting…readers who are interested in applying Bayesian analysis in real case studies may find this book useful. One might consider using this book as supplementary material in graduate-level seminar courses on Bayesian data analysis."

Journal of the American Statistical Association, June 2004

"The volume as a whole illustrates the range of models that can now be fitted following the Markov chain Monte Carlo revolution, assess the behavior of posterior summaries under model misspecifications."

• Center for Statistical Science, Brown University, Providence, USA

  Constantine Gatsonis

• Department Of Statistics, Carnegie Mellon University, Pittsburgh, USA

  Robert E. Kass

• Department of Statistic, Iowa State University, Ames, USA

  Alicia Carriquiry

• Department of Statistics, Columbia University, New York, USA

  Andrew Gelman

• Institute of Statistics and Decision Sciences, Duke University, Durham, USA

  David Higdon

• Program in Biostatistics, Fred Hutchinson Center Research Center, Seattle, USA

  Donna K. Pauler

• Department of Statistics, Carnegie Mellon University, Pittsburgh, USA

  Isabella Verdinelli

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