The SAS GLM and MIXED procedures can be useful for experimenters desiring to analyze data from screening experiments using a member of the class of augmented experiment designs. Since application of the procedures is typically not straightforward for these designs, several programs of possible interest are described. We show how to recover interblocking and intervariety information when the blocking and varieties are random effects, how to arrange varietal responses in descending order, and a number of other options.

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JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. American Statistical Association is collaborating with JSTOR to digitize, preserve and extend access to Journal of the American Statistical Association.Minimum Hellinger distance estimates are considered for finite mixture models when the exact forms of the component densities are unknown in detail but are thought to be close to members of some parametric family. Minimum Hellinger distance estimates are asymptotically efficient if the data come from a member of the parametric family and are robust to certain departures from the parametric family. A new algorithm is introduced that is similar to the EM algorithm, and a specialized adaptive density estimate is also introduced. Standard measures of robustness are discussed, and some difficulties are noted. The robustness and asymptotic efficiency of the estimators are illustrated using simulations.

The minimum description length (MDL) principle articulated in the last decade by Rissanen and his co-workers yields new criteria for statistical model selection. MDL criteria permit data-based choices from among alternative statistical descriptions of data without necessarily assuming that the data were sampled randomly. This article explains the MDL principle informally, indicates the criteria it yields in the common cases of multinomial distributions and Gaussian regression, and illustrates MDL's use with numerical examples. We hope thereby to stimulate experimentation and debate about the pedagogical and practical implications of the MDL approach.

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