Abstract
This chapter provides an overview of the topic of missing data. We introduce the main types of missing data that can occur in practice and discuss the practical consequences of each of these types for general data analysis. We then describe general and practical solutions to the problem of missing data, discussing common but flawed approaches as well as more powerful approaches such as multiple imputation, which is an approach to dealing with missing data that is suitable for many—although not all—situations. Finally, we consider the topic of missing data as part of statistical inference more generally, and how it can be handled in both maximum likelihood and Bayesian approaches to inference.
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Notes
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Continuing with the notation introducted in Sect. 4.2.1, here we will denote the fully observed variables in our data by x, the partially observed variables by \(y = y^{\text {obs}}, y^{\text {obs}}\), and we will index the missing variables in y by the I. We can also assume that any or all of x, y and I may be multivariate arrays.
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Baguley, T., Andrews, M. (2016). Handling Missing Data. In: Robertson, J., Kaptein, M. (eds) Modern Statistical Methods for HCI. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-319-26633-6_4
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