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On the size of the model selection problem in a conjoint measurement experiment
behavioral-neurosciencecomputational-modelingmathematical-psychologyneuroscienceperceptionsocial-and-behavioral-sciences
We examine how the number of models respecting marginality in model selection using Maximum Likelihood Conjoint Measurement (MLCM) varies with the number of dimensions tested and explain the logic for determining this number. The results are related to Dedekind numbers, which increase super-exponen
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