Greedoid-Based Noncompensatory Inference

@inproceedings{Yee2007GreedoidBasedNI,
  title={Greedoid-Based Noncompensatory Inference},
  author={Michael Yee and Ely Dahan and John R. Hauser and James B. Orlin},
  year={2007}
}
Greedoid languages provide a basis to infer best-fitting noncompensatory decision rules from full-rank conjoint data or partial-rank data such as consider-then-rank, consider-only, or choice data. Potential decision rules include elimination by aspects, acceptance by aspects, lexicographic by features, and a mixed-rule lexicographic by aspects (LBA) that nests the other rules. We provide a dynamic program that makes estimation practical for a moderately large numbers of aspects. We test… CONTINUE READING

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