A three-way clusterwise multidimensional unfolding procedure for the spatial representation of context dependent preferences

DOI10.1016/j.csda.2008.04.011zbMath1453.62081OpenAlexW2146950622MaRDI QIDQ961772

Wayne S. Desarbo, Simon J. Blanchard, A. Selin Atalay

Publication date: 1 April 2010

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2008.04.011

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to psychology (62P15)

Related Items (2)

Editorial: Special issue on correspondence analysis and related methods ⋮ Modeling viewpoint shifts in probabilistic choice

Uses Software

• Algorithm 39

Cites Work

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• Multiclus: a new method for simultaneously performing multidimensional scaling and cluster analysis
• GENFOLD 2: A set of models and algorithms for the GENeral unFOLDing analysis of preference/dominance data
• Three-way metric unfolding via alternating weighted least squares
• Correction to: Algorithm 39. Clusterwise linear regression
• CANDELINC: A general approach to multidimensional analysis of many-way arrays with linear constraints on parameters
• A fast algorithm for clusterwise linear regression
• Clusterwise linear regression
• A latent class vector model for preference ratings
• Cluster differences scaling with a within-clusters loss component and a fuzzy successive approximation strategy to avoid local minima
• A latent class unfolding model for analyzing single stimulus preference ratings
• Three-mode component analysis with crisp or fuzzy partition of units
• Simultaneous classification and multidimensional scaling with external information
• Interpreting degenerate solutions in unfolding by use of the vector model and the compensatory distance model
• Avoiding degeneracy in multidimensional unfolding by penalizing on the coefficient of variation
• Evolutionary preference/utility functions: a dynamic perspective
• Points of view analysis revisited: Fitting multidimensional structures to optimal distance components with cluster restrictions on the variables
• Modern multidimensional scaling. Theory and applications.
• The Framing of Decisions and the Psychology of Choice
• Context-Dependent Preferences
• Factorial \(k\)-means analysis for two-way data.

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