Missing data are ubiquitous in clinical epidemiological research. Individuals with missing data may differ from those with no missing data in terms of the outcome of interest and prognosis in general. Missing data are often categorized into the following three types: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). In clinical epidemiological resea...

Missing Data Problems in Machine Learning Benjamin M. Marlin Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2008 Learning, inference, and prediction in the presence of missing data are pervasive problems in machine learning and statistical data analysis. This thesis focuses on the problems of collaborative prediction with non-random missing data and classific...

The performance of five simple multiple imputation methods for dealing with missing data were compared. In addition, random imputation and multivariate normal imputation were used as lower and upper benchmark, respectively. Test data were simulated and item scores were deleted such that they were either missing completely at random, missing at random, or not missing at random. Cronbach's alpha,...

Estimating causal effects from incomplete data requires additional and inherently untestable assumptions regarding the mechanism giving rise to the missing data. We show that using causal diagrams to represent these additional assumptions both complements and clarifies some of the central issues in missing data theory, such as Rubin's classification of missingness mechanisms (as missing complet...

In this document I describe a re-analysis of data from the Southampton Women’s Survey (SWS). The original complete-case analysis implicitly assumed that the data are missing completely at random. Inverse-probability weighting (IPW) and multiple imputation (MI) are more sophisticated methods for handling missing data, which make the weaker assumption that the data are missing at random. We sough...