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BSU Seminar: "Graphical models for missing data not at random: identification, inference, and imputation"

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If you have a question about this talk, please contact Alison Quenault .

This will be a free hybrid seminar. To register to attend remotely, please click here: https://cam-ac-uk.zoom.us/meeting/register/y-ukrQxwTLGSoGtSGiGUkA

Missing data is a pervasive problem in data analyses, resulting in datasets that contain censored realizations of a target distribution. Many approaches to inference on the target distribution using censored observed data rely on missing data models represented as a factorization with respect to a graph.  I describe a simple characterization of all identified missing data models where the full data distribution factorizes with respect to a directed acyclic graph (DAG).  We show how statistical inference may be performed within a maximum likelihood and semi-parametric frameworks in this class of models.  In addition, we discuss how Markov restrictions in his model class naturally lead to an imputation procedure analogues to Gibbs sampling procedures for the missing at random model, such as MICE and Amelia, while allowing imputation even in high dimensional settings where many missingness patterns have no support.

This is joint work with Rohit Bhattacharya, Razieh Nabi, Trung Phung, and Kyle Reese.

This talk is part of the MRC Biostatistics Unit Seminars series.

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Average on weekdays: 8969.30815348 kWh FW11, William Gates Building. Zoom link: https://cl-cam-ac-uk.zoom.us/j/4361570789?pwd=Nkl2T3ZLaTZwRm05bzRTOUUxY3Q4QT09&from=addon BAS Seminar Room 2; https://ukri.zoom.us/j/94382564745

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