International audienceThis 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
Problems of the analysis of data with incomplete observations are all too familiar in statistics. Th...
This report reviews the effects of missing data on probability distributions which covers two main t...
Missing data are inevitably ubiquitous in experimental and observational epidemiological research. N...
International audienceThis short paper discusses the contributions made to the featured section on L...
This paper addresses the following question: how should we update our beliefs after observing some i...
AbstractThis article tries to clarify some aspects of the theory of belief functions especially with...
n Abstract Missing data are a pervasive problem in many public health investiga-tions. The standard ...
Given a parametric statistical model, evidential methods of statistical in-ference aim at constructi...
n Abstract Missing data are a pervasive problem in many public health investiga-tions. The standard ...
International audienceMaximum likelihood is a standard approach to computing a probability distribut...
This paper surveys methods for representing and reasoning with imper-fect information. It opens with...
In real-life situations, we often encounter data sets containing missing observations. Statistical m...
Missing data in scientific research go hand in hand with assumptions about the nature of the missing...
Missing data frequently occurs in quantitative social research. For example, in a survey of individu...
In our model an individual forms beliefs over events based on the frequencies of occurrences of the ...
Problems of the analysis of data with incomplete observations are all too familiar in statistics. Th...
This report reviews the effects of missing data on probability distributions which covers two main t...
Missing data are inevitably ubiquitous in experimental and observational epidemiological research. N...
International audienceThis short paper discusses the contributions made to the featured section on L...
This paper addresses the following question: how should we update our beliefs after observing some i...
AbstractThis article tries to clarify some aspects of the theory of belief functions especially with...
n Abstract Missing data are a pervasive problem in many public health investiga-tions. The standard ...
Given a parametric statistical model, evidential methods of statistical in-ference aim at constructi...
n Abstract Missing data are a pervasive problem in many public health investiga-tions. The standard ...
International audienceMaximum likelihood is a standard approach to computing a probability distribut...
This paper surveys methods for representing and reasoning with imper-fect information. It opens with...
In real-life situations, we often encounter data sets containing missing observations. Statistical m...
Missing data in scientific research go hand in hand with assumptions about the nature of the missing...
Missing data frequently occurs in quantitative social research. For example, in a survey of individu...
In our model an individual forms beliefs over events based on the frequencies of occurrences of the ...
Problems of the analysis of data with incomplete observations are all too familiar in statistics. Th...
This report reviews the effects of missing data on probability distributions which covers two main t...
Missing data are inevitably ubiquitous in experimental and observational epidemiological research. N...
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