Advertisement

Commentary: “Measurement Error Models in Astronomy” by Brandon C. Kelly

  • David Ruppert
Conference paper
Part of the Lecture Notes in Statistics book series (LNS, volume 902)

Abstract

Bayesian analysis offers a general approach to measurement error that has many advantages—it focuses attention on careful modeling, is widely applicable, and provides efficient estimators. Bayesian analysis is relatively easy using WinBUGS software. We discuss here the paper by Brandon Kelly, and present an example of fitting a quadratic regression model with WinBUGS called from R, with the WinBUGS and R code provided.

Keywords

Bayesian Analysis Equation Error Bayesian Estimator Measurement Error Model True Covariate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Carroll, R. J., and Ruppert, D.: The use and misuse of orthogonal regression estimation in linear errors-in-variables models, The American Statistician, 50, 1–6 (1996)Google Scholar
  2. 2.
    Carroll, R.J., Ruppert, D., Stefanski, L., and Crainiceanu, C.M.: Measurement Error in Nonlinear Models: A Modern Perspective, 2nd edn. (Chapman and Hall, New York, 2003)Google Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.School of Operations Research and Information Engineering, Department of Statistical ScienceCornell UniversityIthacaUSA

Personalised recommendations