Accurate non-iterative modelling and inference of longitudinal neuroimaging data

B. Guillaume

Research output: ThesisDoctoral ThesisExternal prepared

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In recent years, increasing efforts have been made to collect longitudinal neuroimaging data in order to study how brains change over time. However, the popular methods used to analyse such kind of data may not always be appropriate (e.g., overly sensitive to model misspecifications, difficult to specify adequately or prohibitively slow to compute) and may sometimes lead to erroneous conclusions. Motivated by these shortcomings, in this dissertation, we have proposed and studied the use of an alternative method, referred to as “the Sandwich Estimator method”, and have demonstrated that it is a fast, easy-to-specify and accurate option to analyse longitudinal or repeated-measures neuroimaging data.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Maastricht University
  • Matthews, Paul, Supervisor
  • Phillips, C., Supervisor, External person
  • Nichols, T.E., Co-Supervisor, External person
Award date30 Sept 2015
Place of PublicationMaastricht
Print ISBNs9789462598515
Publication statusPublished - 2015


  • neuroimaging
  • longitudinal data analysis
  • Sandwich Estimator

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