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  • 1
    Electronic Resource
    Electronic Resource
    Oxford, UK : Blackwell Publishing Ltd
    Journal of sensory studies 3 (1988), S. 0 
    ISSN: 1745-459X
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Chemistry and Pharmacology , Process Engineering, Biotechnology, Nutrition Technology
    Notes: In many attitudinal investigations, particularly those involving free-choice profiling, a very large list of variables or features can emerge. Ordination using generalized Procrustes analysis provides a common base for comparing assessors, but the derived configurations are often high-dimensional and difficult to summarize. This problem can be rectified by selecting a small subset of the original set of variables. Methods of variable selection in principal component analysis can be adapted easily for such purposes, but there is no guarantee with these methods that overall data structure is preserved. A recently introduced variable selection procedure that does aim to preserve the data structure as much as possible would seem to be more appropriate. All methods are described and applied to a set of data arising from an attitudinal investigation of meat products. The results indicate that variable selection should be more widely encouraged.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Journal of classification 2 (1985), S. 277-299 
    ISSN: 1432-1343
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Type of Medium: Electronic Resource
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  • 3
    Electronic Resource
    Electronic Resource
    Springer
    Journal of classification 4 (1987), S. 73-84 
    ISSN: 1432-1343
    Keywords: Classification rules ; Discriminant analysis ; Distance between groups ; Divergence between populations ; Influence functions
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract A distance-based classification procedure suggested by Matusita (1956) has long been available as an alternative to the usual Bayes decision rule. Unsatisfactory features of both approaches when applied to multinomial data led Goldstein and Dillon (1978) to propose a new distance-based principle for classification. We subject the Goldstein/Dillon principle to some theoretical scrutiny by deriving the population classification rules appropriate not only to multinomial data but also to multivariate normal and mixed multinomial/multinormal data. These rules demonstrate equivalence of the Goldstein/Dillon and Matusita approaches for the first two data types, and similar equivalence is conjectured (but not explicitly obtained) for the mixed data case. Implications for sample-based rules are noted.
    Type of Medium: Electronic Resource
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