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1.
This paper develops a new procedure for simultaneously performing multidimensional scaling and cluster analysis on two-way
compositional data of proportions. The objective of the proposed procedure is to delineate patterns of variability in compositions
across subjects by simultaneously clustering subjects into latent classes or groups and estimating a joint space of stimulus
coordinates and class-specific vectors in a multidimensional space. We use a conditional mixture, maximum likelihood framework
with an E-M algorithm for parameter estimation. The proposed procedure is illustrated using a compositional data set reflecting
proportions of viewing time across television networks for an area sample of households. 相似文献
2.
GENFOLD2: A set of models and algorithms for the general UnFOLDing analysis of preference/dominance data 总被引:3,自引:3,他引:0
A general set of multidimensional unfolding models and algorithms is presented to analyze preference or dominance data. This class of models termed GENFOLD2 (GENeral UnFOLDing Analysis-Version 2) allows one to perform internal or external analysis, constrained or unconstrained analysis, conditional or unconditional analysis, metric or nonmetric analysis, while providing the flexibility of specifying and/or testing a variety of different types of unfolding-type preference models mentioned in the literature including Caroll's (1972, 1980) simple, weighted, and general unfolding analysis. An alternating weighted least-squares algorithm is utilized and discussed in terms of preventing degenerate solutions in the estimation of the specified parameters. Finally, two applications of this new method are discussed concerning preference data for ten brands of pain relievers and twelve models of residential communication devices. 相似文献