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On various ways of tackling incomplete information in statistics

Abstract : This short paper discusses the contributions made to the featured section on Low Quality Data. We further refine the distinction between the ontic and epistemic views of imprecise data in statistics. We also question the extent to which likelihood functions can be viewed as belief functions. Finally we comment on the data disambiguation effect of learning methods, relating it to data reconciliation problems.
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Didier Dubois. On various ways of tackling incomplete information in statistics. International Journal of Approximate Reasoning, Elsevier, 2014, vol. 55 (n° 7), pp. 1570-1574. ⟨10.1016/j.ijar.2014.04.002⟩. ⟨hal-01153815⟩

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