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  • Articles  (3)
  • numerical taxonomy  (3)
  • 2000-2004
  • 1970-1974  (3)
  • 1955-1959
  • 1974  (3)
  • Geosciences  (3)
  • Architecture, Civil Engineering, Surveying
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  • Articles  (3)
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  • 2000-2004
  • 1970-1974  (3)
  • 1955-1959
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  • Geosciences  (3)
  • Architecture, Civil Engineering, Surveying
  • Mathematics  (3)
  • 1
    Electronic Resource
    Electronic Resource
    The statistical zap versus the shotgun approach (1974)
    Springer
    Mathematical geology 6 (1974), S. 311-332 
    Details
    ISSN: 1573-8868
    Keywords: canonical correlation ; cluster analysis ; correlation ; discriminant analysis ; factor analysis ; multivariate analysis ; numerical taxonomy ; principal coordinates ; regression analysis ; statistics ; allometry ; eurypterids ; integration and coordination ; ontogeny ; paleontology ; relative growth
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Mathematics
    Notes: Abstract Multivariate analysis is used in the search for one or more types of structure. The statistical zap applies a single method to determine one preselected type of structure. Several zaps suffice to ascertain several types of structure. The statistical shotgun represents an alternative approach. Here, a series of methods is applied to the data with the intent of ascertaining all possible types of structure that may exist. If strong structure is present, an appropriate zap will probably reveal it, and a variety of techniques will determine the same general structure. If only the main structure is required, the zap is adequate. In this situation, the shotgun will display a basic consistency which is at least reassuring. However, zaps may fail to detect a more subtle secondary structure of geological interest which will be displayed by the shotgun. For weakly structured data, a zap will only determine one type of structure but the shotgun reveals all. Study of the ontogeny of Parastylonurus myops(Clarke), a Lower Silurian eurypterid from New York (USA) shows the virtues of the statistical shotgun.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02082354
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    Quantified coefficients of association and measurement of similarity (1974)
    Springer
    Mathematical geology 6 (1974), S. 135-152 
    Details
    ISSN: 1573-8868
    Keywords: classification ; cluster analysis ; numerical taxonomy
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Mathematics
    Notes: Abstract Coefficients of association have been widely employed in cluster analysis. However, their use has been, for the most part, restricted to binary data. This limitation can be overcome by redefining positive and negative matches and mismatches in terms of minimum and maximum values of paired elements of parallel vector arrays. Rewriting the algorithms of coefficients of association with these new components gives the new “quantified” coefficients general utility for binary, ordered multistate, and quantitative data, while retaining their original analytic properties. Quantified coefficients of association avoid several problems of shape and size that are associated with correlation coefficients and measures of Euclidean distance. However, when measuring similarity, quantified coefficients weight each attribute of an object by that attribute's magnitude. A related set of similarity indices termed “mean ratios” is introduced; these indices give each attribute equal weight in all situations. Both quantified coefficients of association and mean ratios are related to a number of measures of similarity introduced to various fields of scientific research during the past 50 years. A review of this literature is included in an attempt to consolidate methodology and simplify nomenclature.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02080152
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  • 3
    Electronic Resource
    Electronic Resource
    A multivariate analytical strategy for classifying paleoenvironments (1974)
    Springer
    Mathematical geology 6 (1974), S. 333-352 
    Details
    ISSN: 1573-8868
    Keywords: classification ; cluster analysis ; discriminant analysis ; multivariate analysis ; ordination ; numerical taxonomy ; paleoecology ; sedimentology
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Mathematics
    Notes: Abstract A multivariate analytical strategy is proposed for aiding the investigator in extracting maximum information from environmental data. Data are carefully coded and scaled and are tested for redundancy using R-mode cluster analysis. The samples are partitioned into environmental classes using Q-mode cluster analysis. Q-mode ordination facilitates interpretations, which usually can be verified by comparison with field relationships. Discriminant analysis serves as an identification procedure for extending the classification to unknown samples. The strategy is demonstrated by application to Cape Hatteras microorganism distributions and Devonian sedimentary facies.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02082355
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