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123, 1978a, table 3, p. 728). e. P = f(s,sh,o,p) for both axes. It should perhaps be emphasized that the same features will also be characteristic of all measures made in thin section and, by analogy, on a map or picture using these procedures; it seems likely that the measure tends to ordinal level.

Also we obtain the maximum likelihood estimators of the parameters in the model as in Jorgenson's model. This Poisson model can be considered as a special * Geological Survey of Canada Contribution Number 16787 29 C. F. Chung et al. ), Quantitative Analysis of Mineral and Energy Resources, 29-36. © 1988 by D. Reidel Publishing Company. 30 case of the class of the generalized linear models described by McCullagh and Nelder (1983). We first review Jorgenson's Poisson model and its assumptions. We then discuss the general linear Poisson model and its application to the data for volcanic massive sulphide deposits in Abitibi area of the Canadian Shield (Table 2 of Agterberg elsewhere in this volume).

B. , 1984, K-clustering as a detection tool for influential subsets in regression; Technometrics, v. 26, pp. 305-318. , 1984, Mineral resources appraisal; Oxford University Press, New York, 445 p. Hartigan, J. A. , 1981, Consistency of single linkage for high-density clusters; Jour. American Stat. , v. 16, pp. 388-394. C. , 1978, The hat matrix in regression and ANOVA; American Statistician, v. 32, no. 1, pp. 17-22. , 1983, Developments in linear regression methodology: 1959-1982; Technometrics, v.

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