Bayesian Analysis

Comment on article by Craigmile et al.

Christopher David Barr and Francesca Dominici

Full-text: Open access

Abstract

We congratulate the authors on their development of Bayesian hierarchical models that simultaneously overcome many of the challenges inherent to analyzing exposure pathways to Arsenic. Drs. Craigmile, Calder, Li, Paul and Cressie (herein referenced by CCLPC) address a range of obstacles with a sound mix of sophisticated models, useful plots and common sense. Their paper is well written throughout, making important illustrative, methodological and applied contributions. We thank the editor for inviting us to discuss this stimulating paper, and will begin by calling special attention to some particular highlights of the analysis by CCLPC. After which, we will mention a few aspects we believe can be further improved.

Article information

Source
Bayesian Anal., Volume 4, Number 1 (2009), 37-39.

Dates
First available in Project Euclid: 22 June 2012

Permanent link to this document
https://projecteuclid.org/euclid.ba/1340370386

Digital Object Identifier
doi:10.1214/09-BA401A

Mathematical Reviews number (MathSciNet)
MR2486235

Zentralblatt MATH identifier
1330.62377

Citation

Barr, Christopher David; Dominici, Francesca. Comment on article by Craigmile et al. Bayesian Anal. 4 (2009), no. 1, 37--39. doi:10.1214/09-BA401A. https://projecteuclid.org/euclid.ba/1340370386


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References

  • Gelman, A., Pasarica, C., and Dodhia, R. (2002). “Lets practice what we preach: Turning tables into graphs.” American Statistician, 56: 121–130.
  • Peng, R., Dominici, F., and Zeger, S. (2006). “Reproducible epidemiological research.” American Journal of Epidemiology, 163: 783–789.

See also

  • Related item: Catherine A. Calder, Peter F. Craigmile, Noel Cressie, Hongfei Li, Rajib Paul. Hierarchical model building, fitting, and checking: a behind-the-scenes look at a Bayesian analysis of arsenic exposure pathways. Bayesian Anal., Vol. 4, Iss. 1 (2009), 1-35.