You are not currently logged in.
Access JSTOR through your library or other institution:
If You Use a Screen ReaderThis content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Contaminant Detection in the Visual Inspection of Seed Samples
C. G. G. Aitken, J. Shaw and M. Talbot
Journal of the Royal Statistical Society. Series C (Applied Statistics)
Vol. 44, No. 4 (1995), pp. 431-440
Stable URL: http://www.jstor.org/stable/2986136
Page Count: 10
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Preview not available
The quality of a seed lot is determined, in part, by the amount of contaminants in a sample. One way of automating the process of identifying contaminants is to take shape and size measurements of each item in the sample. The process of detecting contaminants from such data may be viewed as a multivariate statistical outlier problem in which contaminants are considered as outliers in a sample of normal seeds. Pettit has developed Bayesian diagnostics for multivariate normal distributions with multivariate normal prior distributions; an extension is described here in which the prior distribution for the mean is other than normal and is represented by a kernel density estimate. The performances of the normal distribution and extended methods are compared in an application to the seed testing problem.
Journal of the Royal Statistical Society. Series C (Applied Statistics) © 1995 Royal Statistical Society