Couso, Inès and Dubois, Didier Maximum Likelihood Under Incomplete Information: Toward a Comparison of Criteria. (2016) In: 8th International Conference on Soft Methods in Probability and Statistics (SMPS 2016), 12 September 2016 - 14 September 2016 (Roma, Italy).
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(Document in French)
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Official URL: http://dx.doi.org/10.1007/978-3-319-42972-4_18
Abstract
Maximum likelihood is a standard approach to computing a probability distribution that best fits a given dataset. However, when datasets are incomplete or contain imprecise data, depending on the purpose, a major issue is to properly define the likelihood function to be maximized. This paper compares several proposals in terms of their intuitive appeal, showing their anomalous behavior on examples.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Additional Information: | Thanks to Springer editor. This papers appears in Volume 456 of the series Advances in Intelligent Systems and Computing ISSN: 2194-5357 The original PDF is available at: http://link.springer.com/chapter/10.1007/978-3-319-42972-4_18 |
| Audience (conference): | International conference proceedings |
| Uncontrolled Keywords: | |
| Institution: | French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE) Université de Toulouse > Institut National Polytechnique de Toulouse - INPT (FRANCE) Université de Toulouse > Université Toulouse III - Paul Sabatier - UPS (FRANCE) Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE) Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE) Other partners > Universidad de Oviedo (SPAIN) |
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| Statistics: | download |
| Deposited By: | IRIT IRIT |
| Deposited On: | 11 Jan 2017 16:06 |
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