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  • 1
    Electronic Resource
    Electronic Resource
    Fitting an extended INDSCAL model to three-way proximity data (1995)
    Springer
    Journal of classification 12 (1995), S. 57-71 
    Details
    ISSN: 1432-1343
    Keywords: Weighted Euclidean model ; INDSCAL ; Multidimensional scaling ; Specificities ; Monotone splines
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract The INDSCAL individual differences scaling model is extended by assuming dimensions specific to each stimulus or other object, as well as dimensions common to all stimuli or objects. An “alternating maximum likelihood” procedure is used to seek maximum likelihood estimates of all parameters of this EXSCAL (Extended INDSCAL) model, including parameters of monotone splines assumed in a “quasi-nonmetric” approach. The rationale for and numerical details of this approach are described and discussed, and the resulting EXSCAL method is illustrated on some data on perception of musical timbres.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01202267
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    A latent class approach to fitting the weighted Euclidean model, clascal (1993)
    Springer
    Psychometrika 58 (1993), S. 315-330 
    Details
    ISSN: 1860-0980
    Keywords: weighted Euclidean distance model ; INDSCAL ; latent class analysis ; mixture distribution model ; EM algorithm
    Source: Springer Online Journal Archives 1860-2000
    Topics: Psychology
    Notes: Abstract A weighted Euclidean distance model for analyzing three-way proximity data is proposed that incorporates a latent class approach. In this latent class weighted Euclidean model, the contribution to the distance function between two stimuli is per dimension weighted identically by all subjects in the same latent class. This model removes the rotational invariance of the classical multidimensional scaling model retaining psychologically meaningful dimensions, and drastically reduces the number of parameters in the traditional INDSCAL model. The probability density function for the data of a subject is posited to be a finite mixture of spherical multivariate normal densities. The maximum likelihood function is optimized by means of an EM algorithm; a modified Fisher scoring method is used to update the parameters in the M-step. A model selection strategy is proposed and illustrated on both real and artificial data.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02294578
    Library Location Call Number Volume/Issue/Year Availability
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    Paper (German National Licenses)
    Fulltext
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