Dubois, Didier On various ways of tackling incomplete information in statistics. (2014) International Journal of Approximate Reasoning, 55 (7). 1570-1574. ISSN 0888-613X
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(Document in English)
PDF (Author's version) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader 157kB |
Official URL: http://dx.doi.org/10.1016/j.ijar.2014.04.002
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.
| Item Type: | Article |
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| Additional Information: | Thanks to Elsevier editor. The definitive version is available at http://www.sciencedirect.com The original PDF of the article can be found at International Journal of Approximate Reasoning website : http://www.sciencedirect.com/science/article/pii/S0888613X14000541 |
| HAL Id: | hal-01153815 |
| Audience (journal): | International peer-reviewed journal |
| Uncontrolled Keywords: | |
| Institution: | Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE) French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE) Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE) Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE) Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE) |
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| Statistics: | download |
| Deposited On: | 16 Feb 2015 14:35 |
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