If you need an accessible version of this item please contact JSTOR User Support

A Predictive Approach to the Analysis of Designed Experiments

Joseph G. Ibrahim and Purushottam W. Laud
Journal of the American Statistical Association
Vol. 89, No. 425 (Mar., 1994), pp. 309-319
DOI: 10.2307/2291227
Stable URL: http://www.jstor.org/stable/2291227
Page Count: 11
  • Download PDF
  • Cite this Item

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

If you need an accessible version of this item please contact JSTOR User Support
A Predictive Approach to the Analysis of Designed Experiments
Preview not available

Abstract

Viewing the analysis of designed experiments as a model selection problem, we introduce the use of a predictive Bayesian criterion in this context based on the predictive density of a replicate experiment (PDRE). A calibration of the criterion is provided to assist in the model choice. The relationships of the proposed criterion to other prevalent criteria, such as AIC, BIC, and Mallows's Cp, are given. An information theoretic criterion based on the PDRE's of two competing models is also introduced and compared with the usual F statistic for two nested models. Examples are given to illustrate the proposed methodology.

Page Thumbnails

  • Thumbnail: Page 
309
    309
  • Thumbnail: Page 
310
    310
  • Thumbnail: Page 
311
    311
  • Thumbnail: Page 
312
    312
  • Thumbnail: Page 
313
    313
  • Thumbnail: Page 
314
    314
  • Thumbnail: Page 
315
    315
  • Thumbnail: Page 
316
    316
  • Thumbnail: Page 
317
    317
  • Thumbnail: Page 
318
    318
  • Thumbnail: Page 
319
    319