Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.720202
Title: On the use of hierarchical models for multiple imputation and synthetic data generation
Author: Rashid, Sana
ISNI:       0000 0004 6347 8095
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2017
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Thesis embargoed until 30 Apr 2020
Access from Institution:
Abstract:
Missing data are often imputed with plausible values when various analyses are performed. One popular approach employed to impute data is multiple imputation, which requires specification of a suitable imputation model. This thesis investigates the impact on multiply imputed hierarchical datasets when the imputation model is misspecified. The first issue studied is the presence of omitted variable bias. The same issue is then studied with a focus on the use of multiple imputation for creating synthetic data to protect data confidentiality. Here, the quality of multiply imputed datasets is studied not only through performance of various analysis models, but also, risks of disclosure for sensitive data. With the help of simulation studies and a longitudinal dataset from establishments in Germany, the detrimental effect of such model misspecification is evaluated, and recommendations are made for users of multiple imputation for both missing and synthetic data. The second issue investigated is model misspecification due to incorrect modelling of the shape of the error term. Existing methods for robust regression and alternatives to the normal distribution are compared within the synthetic data context only. Results from simulation studies and data on household wealth in the UK are used to identify appropriate methods for multiple imputation in such a scenario.
Supervisor: Mitra, Robin ; Kouris, Nikos Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.720202  DOI: Not available
Share: