OATAO - Open Archive Toulouse Archive Ouverte Open Access Week

Maximum Likelihood Under Incomplete Information: Toward a Comparison of Criteria

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).

[img] (Document in French)

PDF (Author's version) - Depositor and staff only - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
159kB

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)
Laboratory name:
Statistics:download
Deposited By: IRIT IRIT
Deposited On:11 Jan 2017 16:06

Repository Staff Only: item control page