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Monotonicity as a tool for differentiating between truth and optimality in the aggregation of rankings

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Keywords
Social choice, Monotonicity, Monometric, Maximum likelihood estimator, LINEAR INEQUALITY CONSTRAINTS, LIKELIHOOD RATIO, RULE BASES, MODELS, SET, HYPOTHESES, ALGORITHMS

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Citation

Please use this url to cite or link to this publication:

Chicago
Perez Fernandez, Raul, Pedro Alonso, Irene Díaz, Susana Montes, and Bernard De Baets. 2017. “Monotonicity as a Tool for Differentiating Between Truth and Optimality in the Aggregation of Rankings.” Journal of Mathematical Psychology 77: 1–9.
APA
Perez Fernandez, R., Alonso, P., Díaz, I., Montes, S., & De Baets, B. (2017). Monotonicity as a tool for differentiating between truth and optimality in the aggregation of rankings. JOURNAL OF MATHEMATICAL PSYCHOLOGY, 77, 1–9.
Vancouver
1.
Perez Fernandez R, Alonso P, Díaz I, Montes S, De Baets B. Monotonicity as a tool for differentiating between truth and optimality in the aggregation of rankings. JOURNAL OF MATHEMATICAL PSYCHOLOGY. 2017;77:1–9.
MLA
Perez Fernandez, Raul et al. “Monotonicity as a Tool for Differentiating Between Truth and Optimality in the Aggregation of Rankings.” JOURNAL OF MATHEMATICAL PSYCHOLOGY 77 (2017): 1–9. Print.
@article{8517550,
  author       = {Perez Fernandez, Raul and Alonso, Pedro and D{\'i}az, Irene and Montes, Susana and De Baets, Bernard},
  issn         = {0022-2496},
  journal      = {JOURNAL OF MATHEMATICAL PSYCHOLOGY},
  language     = {eng},
  pages        = {1--9},
  title        = {Monotonicity as a tool for differentiating between truth and optimality in the aggregation of rankings},
  url          = {http://dx.doi.org/10.1016/j.jmp.2017.01.003},
  volume       = {77},
  year         = {2017},
}

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