Training and Events
Methods for Missing Data
Dr Gareth Ridall
05/03/2018 - 06/03/2018
Department of Mathematics and Statistics
View in Google Maps (LA1 4YF)
Angela Mercer, 01524 593064, email@example.com
This module deals with the ubiquitous and often neglected problem of dealing with missing data, common in many types of statistical analysis. We survey some ad-hoc strategies to deal with them and show how the can lead to bias and inefficiencies. We advocate using a principled approach and the formulating of the inherent missing data mechanism. We look at several principled methods of dealing with missing data. First we present a fully Bayesian approach using Winbugs. Secondly we create multiply imputed datasets using chained equation and then apply Rubin’s rules for combing the analyses of the models. We then do the same thing as the previous method but use multivariate techniques rather than chained equations as the method of multiple imputation. Finally we look at examples where no imputation is needed at all. All of the methods will be illustrated through good examples using the appropriate tools for exploration and diagnostics. We will also touch on models for imputation for hierarchical models when a mixed effects.
Intermediate (some prior knowledge)
External from industry/Commerce, £540 and External from academic institution/public sector/charity staff £460; External postgraduate student £300.
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