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  • 1
    Electronic Resource
    Electronic Resource
    Multidimensional Scaling (1980)
    Palo Alto, Calif. : Annual Reviews
    Annual Review of Psychology 31 (1980), S. 607-649 
    Details
    ISSN: 0066-4308
    Source: Annual Reviews Electronic Back Volume Collection 1932-2001ff
    Topics: Psychology
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1146/annurev.ps.31.020180.003135
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    Evaluation of monoclonal anti-A and anti-B and affinity-purified Ulex europaeus lectin I for forensic blood grouping (1984)
    Springer
    International journal of legal medicine 93 (1984), S. 259-268 
    Details
    ISSN: 1437-1596
    Keywords: Monoclonal antibody ; AB0 system, bloodstains ; Absorption inhibition, bloodstains ; Absorption elution, bloodstains ; Blutgruppen, monoklonale Antikörper ; Anti-H, Ulex-Präparate
    Source: Springer Online Journal Archives 1860-2000
    Topics: Medicine , Law
    Description / Table of Contents: Zusammenfassung Zwei verschiedene monoklonale Anti-A- und Anti-B-Reagenzien sowie verschiedene Lectin-I-Präparate, durch Affinitätschromatographie aus Ulex-europaeus-Samen gereinigt, wurden mit üblichen Anti-A- und Anti-B-Seren sowie Ulex-Anti-H hinsichtlich ihrer serologischen Eigenschaften im Hemmtest mit Sekretor-Speichel und im Elutionstest bei Blutspuren miteinander verglichen. Die monoklonalen Reagenzien und Ulex-Präparate sind mit den bisher üblichen Reagenzien durchaus vergleichbar und eignen sich gleichermaßen für forensische Hemm- und Elutionstests.
    Notes: Summary Two different monoclonal anti-A and anti-B and several different affinity purified Ulex europaeus lectin I reagents were evaluated and compared with conventional anti-A and anti-B sera and Ulex anti-H for serologic properties, in inhibition tests with secretor salivas, and in elution tests with bloodstains. The monoclonal and purified reagents were found to be comparable to conventional ones, and accordingly suitable for forensic inhibition and elution procedures.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00198651
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    Paper (German National Licenses)
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  • 3
    Electronic Resource
    Electronic Resource
    The representation of three-way proximity data by single and multiple tree structure models (1984)
    Springer
    Journal of classification 1 (1984), S. 25-74 
    Details
    ISSN: 1432-1343
    Keywords: Clustering ; Alternating least squares ; Discrete optimization
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract Models for the representation of proximity data (similarities/dissimilarities) can be categorized into one of three groups of models: continuous spatial models, discrete nonspatial models, and hybrid models (which combine aspects of both spatial and discrete models). Multidimensional scaling models and associated methods, used for thespatial representation of such proximity data, have been devised to accommodate two, three, and higher-way arrays. At least one model/method for overlapping (but generally non-hierarchical) clustering called INDCLUS (Carroll and Arabie 1983) has been devised for the case of three-way arrays of proximity data. Tree-fitting methods, used for thediscrete network representation of such proximity data, have only thus far been devised to handle two-way arrays. This paper develops a new methodology called INDTREES (for INdividual Differences in TREE Structures) for fitting various(discrete) tree structures to three-way proximity data. This individual differences generalization is one in which different individuals, for example, are assumed to base their judgments on the same family of trees, but are allowed to have different node heights and/or branch lengths. We initially present an introductory overview focussing on existing two-way models. The INDTREES model and algorithm are then described in detail. Monte Carlo results for the INDTREES fitting of four different three-way data sets are presented. In the application, a single ultrametric tree is fitted to three-way proximity data derived from intention-to-buy-data for various brands of over-the-counter pain relievers for relieving three common types of maladies. Finally, we briefly describe how the INDTREES procedure can be extended to accommodate hybrid modelling, as well as to handle other types of applications.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01890116
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    Paper (German National Licenses)
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  • 4
    Electronic Resource
    Electronic Resource
    Candelinc: A general approach to multidimensional analysis of many-way arrays with linear constraints on parameters (1980)
    Springer
    Psychometrika 45 (1980), S. 3-24 
    Details
    ISSN: 1860-0980
    Keywords: constrained least-squares ; multilinear models ; bilinear models ; INDSCAL ; multidimensional scaling ; 3-mode factor analysis ; CANDECOMP ; LINCINDS ; multivariate analysis
    Source: Springer Online Journal Archives 1860-2000
    Topics: Psychology
    Notes: Abstract Very general multilinear models, called CANDELINC, and a practical least-squares fitting procedure, also called CANDELINC, are described for data consisting of a many-way array. The models incorporate the possibility of general linear constraints, which turn out to have substantial practical value in some applications, by permitting better prediction and understanding. Description of the model, and proof of a theorem which greatly simplifies the least-squares fitting process, is given first for the case involving two-way data and a bilinear model. Model and proof are then extended to the case ofN-way data and anN-linear model for generalN. The caseN = 3 covers many significant applications. Two applications are described: one of two-way CANDELINC, and the other of CANDELINC used as a constrained version of INDSCAL. Possible additional applications are discussed.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02293596
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    Paper (German National Licenses)
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  • 5
    Electronic Resource
    Electronic Resource
    Mapclus: A mathematical programming approach to fitting the adclus model (1980)
    Springer
    Psychometrika 45 (1980), S. 211-235 
    Details
    ISSN: 1860-0980
    Keywords: additive clustering ; nonhierarchical clustering ; alternating least squares
    Source: Springer Online Journal Archives 1860-2000
    Topics: Psychology
    Notes: Abstract We present a new algorithm, MAPCLUS (MAthematicalProgrammingCLUStering), for fitting the Shepard-Arabie ADCLUS (forADditiveCLUStering) model. MAPCLUS utilizes an alternating least squares method combined with a mathematical programming optimization procedure based on a penalty function approach, to impose discrete (0,1) constraints on parameters defining cluster membership. This procedure is supplemented by several other numerical techniques (notably a heuristically based combinatorial optimization procedure) to provide an efficient general-purpose computer implemented algorithm for obtaining ADCLUS representations. MAPCLUS is illustrated with an application to one of the examples given by Shepard and Arabie using the older ADCLUS procedure. The MAPCLUS solution uses half as many clusters to achieve nearly the same level of goodness-of-fit. Finally, we consider an extension of the present approach to fitting a three-way generalization of the ADCLUS model, called INDCLUS (INdividualDifferencesCLUStering).
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02294077
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    Paper (German National Licenses)
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  • 6
    Electronic Resource
    Electronic Resource
    Nonmetric maximum likelihood multidimensional scaling from directional rankings of similarities (1981)
    Springer
    Psychometrika 46 (1981), S. 389-405 
    Details
    ISSN: 1860-0980
    Keywords: rank orders ; model selection ; AIC ; face perception
    Source: Springer Online Journal Archives 1860-2000
    Topics: Psychology
    Notes: Abstract A maximum likelihood estimation procedure is developed for multidimensional scaling when (dis)similarity measures are taken by ranking procedures such as the method of conditional rank orders or the method of triadic combinations. The central feature of these procedures may be termed directionality of ranking processes. That is, rank orderings are performed in a prescribed order by successive first choices. Those data have conventionally been analyzed by Shepard-Kruskal type of nonmetric multidimensional scaling procedures. We propose, as a more appropriate alternative, a maximum likelihood method specifically designed for this type of data. A broader perspective on the present approach is given, which encompasses a wide variety of experimental methods for collecting dissimilarity data including pair comparison methods (such as the method of tetrads) and the pick-M method of similarities. An example is given to illustrate various advantages of nonmetric maximum likelihood multidimensional scaling as a statistical method. At the moment the approach is limited to the case of one-mode two-way proximity data, but could be extended in a relatively straightforward way to two-mode two-way, two-mode three-way or even three-mode three-way data, under the assumption of such models as INDSCAL or the two or three-way unfolding models.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02293797
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    Paper (German National Licenses)
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  • 7
    Electronic Resource
    Electronic Resource
    Spatial versus tree representations of proximity data (1982)
    Springer
    Psychometrika 47 (1982), S. 3-24 
    Details
    ISSN: 1860-0980
    Keywords: multidimensional scaling ; clustering ; tree structures ; additive trees
    Source: Springer Online Journal Archives 1860-2000
    Topics: Psychology
    Notes: Abstract In this paper we investigated two of the most common representations of proximities, two-dimensional euclidean planes and additive trees. Our purpose was to develop guidelines for comparing these representations, and to discover properties that could help diagnose which representation is more appropriate for a given set of data. In a simulation study, artificial data generated either by a plane or by a tree were scaled using procedures for fitting either a plane (KYST) or a tree (ADDTREE). As expected, the appropriate model fit the data better than the inappropriate model for all noise levels. Furthermore, the two models were roughly comparable: for all noise levels, KYST accounted for plane data about as well as ADDTREE accounted for tree data. Two properties of the data proved useful in distinguishing between the models: the skewness of the distribution of distances, and the proportion of elongated triangles, which measures departures from the ultrametric inequality, Applications of KYST and ADDTREE to some twenty sets of real data, collected by other investigators, showed that most of these data could be classified clearly as favoring either a tree or a two-dimensional representation.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02293848
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    Paper (German National Licenses)
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  • 8
    Electronic Resource
    Electronic Resource
    Indclus: An individual differences generalization of the adclus model and the mapclus algorithm (1983)
    Springer
    Psychometrika 48 (1983), S. 157-169 
    Details
    ISSN: 1860-0980
    Keywords: additive clustering ; nonhierarchical clustering ; combinatorial optimization ; three-way clustering ; individual differences clustering
    Source: Springer Online Journal Archives 1860-2000
    Topics: Psychology
    Notes: Abstract We present a new model and associated algorithm, INDCLUS, that generalizes the Shepard-Arabie ADCLUS (ADditive CLUStering) model and the MAPCLUS algorithm, so as to represent in a clustering solution individual differences among subjects or other sources of data. Like MAPCLUS, the INDCLUS generalization utilizes an alternating least squares method combined with a mathematical programming optimization procedure based on a penalty function approach to impose discrete (0,1) constraints on parameters defining cluster membership. All subjects in an INDCLUS analysis are assumed to have a common set of clusters, which are differentially weighted by subjects in order to portray individual differences. As such, INDCLUS provides a (discrete) clustering counterpart to the Carroll-Chang INDSCAL model for (continuous) spatial representations. Finally, we consider possible generalizations of the INDCLUS model and algorithm.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02294012
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    Paper (German National Licenses)
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  • 9
    Electronic Resource
    Electronic Resource
    A maximum likelihood method for fitting the wandering vector model (1983)
    Springer
    Psychometrika 48 (1983), S. 553-566 
    Details
    ISSN: 1860-0980
    Keywords: preference ; paired comparisons data ; maximum likelihood multidimensional scaling ; method of successive categories
    Source: Springer Online Journal Archives 1860-2000
    Topics: Psychology
    Notes: Abstract After introducing some extensions of a recently proposed probabilistic vector model for representing paired comparisons choice data, an iterative procedure for obtaining maximum likelihood estimates of the model parameters is developed. The possibility of testing various hypotheses by means of likelihood ratio tests is discussed. Finally, the algorithm is applied to some existing data sets for illustrative purposes.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02293879
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    Paper (German National Licenses)
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  • 10
    Electronic Resource
    Electronic Resource
    Synthesized clustering: A method for amalgamating alternative clustering bases with differential weighting of variables (1984)
    Springer
    Psychometrika 49 (1984), S. 57-78 
    Details
    ISSN: 1860-0980
    Keywords: Cluster Analysis ; Variable Importance
    Source: Springer Online Journal Archives 1860-2000
    Topics: Psychology
    Notes: Abstract In the application of clustering methods to real world data sets, two problems frequently arise: (a) how can the various contributory variables in a specific battery be weighted so as to enhance some cluster structure that may be present, and (b) how can various alternative batteries be combined to produce a single clustering that “best” incorporates each contributory set. A new method is proposed (SYNCLUS, SYNthesizedCLUStering) for dealing with these two problems.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02294206
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