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Missing Ordinal Covariate with Informative Selection [PDF]
Journal of the Royal Statistical Society Series A: Statistics in Society, 2013 SummaryThe paper considers the problem of parameter estimation in a model for a continuous response variable Y when an ordinal explanatory variable X is missing for a substantial proportion of the sample and the selection mechanism (non-deletion from the sample) S depends on unobservables after conditioning on all explanatory variables—i.e.
Alfonso Miranda, Sophia Rabe-Hesketh
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Correcting for missing data in information cascades [PDF]
Proceedings of the fourth ACM international conference on Web search and data mining, 2011 Transmission of infectious diseases, propagation of information, and spread of ideas and influence through social networks are all examples of diffusion. In such cases we say that a contagion spreads through the network, a process that can be modeled by a cascade graph. Studying cascades and network diffusion is challenging due to missing data.
Eldar Sadikov +3 more
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Mending the big-data missing information [PDF]
2016 IEEE International Conference on the Science of Electrical Engineering (ICSEE), 2016 Consider a high-dimensional data set, in which for every data-point there is incomplete information. Each object in the data set represents a real entity, which is described by a point in high-dimensional space. We model the lack of information for a given object as an affine subspace in $\mathbb{R}^d$ whose dimension $k$ is the number of missing ...
Daltrophe, Hadassa +2 more
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Predicting Missing Information of Vulnerability Reports
Companion Proceedings of the Web Conference 2020, 2020 We have found that there is a certain degree of missing information through studying the current exposure of software vulnerabilities. This problem is caused by incomplete information submitted by the vulnerability report submitter. In this paper, we extract the knowledge of software vulnerability in a fine-grained way, and design a machine learning ...
Hao Guo +2 more
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