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.
Future Internet
KnowledgeManagementMultimedia &
Information SystemsNarrative
HypermediaNew Media SystemsSemantic Web &
Knowledge ServicesSocial Software
Future Internet is...

To succeed the Future Internet will need to address a number of cross-cutting challenges including:
- Scalability in the face of peer-to-peer traffic, decentralisation, and increased openness
- Trust when government, medical, financial, personal data are increasingly trusted to the cloud, and middleware will increasingly use dynamic service selection
- Interoperability of semantic data and metadata, and of services which will be dynamically orchestrated
- Pervasive usability for users of mobile devices, different languages, cultures and physical abilities
- Mobility for users who expect a seamless experience across spaces, devices, and velocities
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