Tech Reports
Tech Report kmi-98-06 Abstract
Model Selection and Model Averaging with Missing Data
Techreport ID: kmi-98-06
Date: 1998
Author(s): Marco Ramoni and Paola Sebastiani
Missing data can impair the reliability of statistical inference as they may affect the representativity of the sample. Nonetheless, under some conditions guaranteeing that the missing data mechanism is ignorable, reliable conclusions can be still drawn from the incomplete sample. Ignorability conditions are well-understood for parameter estimation but when the inference task involves the computation of the posterior probability of a data model, as required by Bayesian model selection and prediction through model averaging, these conditions are not sufficient. This paper defines new ignorability conditions for model selection and model averaging from incomplete data. 1. Knowledge Media Institute, The Open University. 2. Department of Actuarial Science and Statistics, City University.
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Interacting with other people not only forms the core of human social and psychological experience, but also lies at the centre of what makes the internet such a rich, powerful and exciting collection of knowledge media. We are especially interested in what happens when such interactions take place on a very large scale -- not only because we work regularly with tens of thousands of distance learners at the Open University, but also because it is evident that being part of a crowd in real life possesses a certain 'buzz' of its own, and poses a natural challenge. Different nuances emerge in different user contexts, so we choose to investigate the contexts of work, learning and play to better understand the trade-offs involved in designing effective large-scale social software for multiple purposes.
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