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Ambiguity and robust statistics

Author

Listed:
  • Cerreia-Vioglio, Simone
  • Maccheroni, Fabio
  • Marinacci, Massimo
  • Montrucchio, Luigi

Abstract

Starting with the seminal paper of Gilboa and Schmeidler (1989) [32] an analogy between the maxmin approach of decision theory under ambiguity and the minimax approach of robust statistics – e.g., Blum and Rosenblatt (1967) [10] – has been hinted at. The present paper formally clarifies this relation by showing the conditions under which the two approaches are actually equivalent.

Suggested Citation

  • Cerreia-Vioglio, Simone & Maccheroni, Fabio & Marinacci, Massimo & Montrucchio, Luigi, 2013. "Ambiguity and robust statistics," Journal of Economic Theory, Elsevier, vol. 148(3), pages 974-1049.
  • Handle: RePEc:eee:jetheo:v:148:y:2013:i:3:p:974-1049
    DOI: 10.1016/j.jet.2012.10.003
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    • Simone Cerreia-Vioglio & Fabio Maccheroni & Massimo Marinacci & Luigi Montrucchio, 2011. "Ambiguity and Robust Statistics," Working Papers 382, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    References listed on IDEAS

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    1. Peter Klibanoff & Massimo Marinacci & Sujoy Mukerji, 2005. "A Smooth Model of Decision Making under Ambiguity," Econometrica, Econometric Society, vol. 73(6), pages 1849-1892, November.
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    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Ambiguity; Consistency; Minimax estimation; Robust statistics; Uncertainty aversion; Variational preferences;

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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