Estimation of the linear mixed integrated Ornstein-Uhlenbeck model

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Abstract

The linear mixed model with an added integrated Ornstein-Uhlenbeck (IOU) process (linear mixed IOU model) allows for serial correlation and estimation of the degree of derivative tracking. It is rarely used, partly due to the lack of available software. We implemented the linear mixed IOU model in Stata and using simulations we assessed the feasibility of fitting the model by restricted maximum likelihood when applied to balanced and unbalanced data. We compared different (1) optimization algorithms, (2) parameterizations of the IOU process, (3) data-structures and (4) random-effects structures. Fitting the model was practical and feasible when applied to large and moderately sized balanced datasets (20000 and 500 observations), and large unbalanced datasets with (non-informative) dropout and intermittent missingness. Analysis of a real dataset showed that the linear mixed IOU model was a better fit to the data than the standard linear mixed model (i.e. independent within-subject errors with constant variance).
Original languageEnglish
Pages (from-to)1541-1558
Number of pages18
JournalJournal of Statistical Computation and Simulation
Volume87
Issue number8
Early online date12 Jan 2017
DOIs
Publication statusPublished - May 2017

Structured keywords

  • Jean Golding

Keywords

  • Fixed effects
  • Newton Raphson
  • Integrated Ornstein-Uhlenbeck process
  • Random effects
  • Repeated measures

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