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J C Gower

Publications and source records attributed to J C Gower.

5 recordsLinked to original sources
PubMed1998

The role of constraints in determining optimal scores.

This paper is about Guttman's method for assigning optimal numerical scores to categorical variables, and related methods, which underlie Healy's TW1 and TW2 bone-age standards. In giving the methodological underpinning, Healy and Goldstein noted that the scores they obtained could depend critically on how essential constraints were specified, thus questioning the whole of optimal score theory. This paper is concerned with resolving this difficulty; the resolution is surprisingly intricate, involving: (i) the relationship between those optimality criteria expressed as ratios and those not; (ii) the distinction between weak constraints (identification constraints) and strong constraints; (iii) the relationship between working in terms of deviations from the mean scores and so-called 'uninteresting solutions'; (iv) the use of short-cut algorithms that yield admissible solutions only when the correct strong constraints are applied; (v) generalizations that lead to reformulations of classical multivariate methods with algorithmic as well as statistical consequences.

Age Determination by Skeleton↗
PubMed1982

Models for the analysis of interregional migration.

"Asymmetric square tables, such as those arising from interregional migration, can be analysed by separating the skew-symmetric and symmetric components. A least-squares analysis of the skew-symmetric part can indicate the degree of complexity of model that is consistent with data and this can be combined with some suitable model for the symmetric part. The joint model may then be fitted by maximum likelihood based on suitable distributional assumptions. This approach is used for an analysis of Australian interstate migration for l960-l966 and indicates a model with independent in-migration and out-migration rates proportional to a symmetric function of population sizes and interstate distance."

Australia↗
PubMed1975

A maximal predictive classification of Klebsielleae and of the yeasts.

The concepts of the numerical method of maximal predictive classification are illustrated with classifications of 13 species of enterobacteria and of 434 species of yeast. The method seeks to classify into a specified number of classes (k) such that more correct statements can be made about the constituent members than with any other classification. The best choice of k relates to the separation of the classes as measured by the average number of correct statements made for an individual assigned to a class to which it does not belong. The maximal predictive classifications are compared with previous classifications of the two groups, which seem to be poor predictively (in terms of the characters considered in this study). The results suggest that taxonomists may be more concerned with maximizing class separation rather than with prediction, but many more groups of organisms would need similar study before this view could be held with confidence.

Aerobiosis↗