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Forecasting multivariate longitudinal binary data with marginal and marginally specified models

Research output: Contribution to Journal/MagazineJournal articlepeer-review

  • Özgür Asar
  • Ozlem Ilk
<mark>Journal publication date</mark>2015
<mark>Journal</mark>Journal of Statistical Computation and Simulation
Issue number2
Number of pages16
Pages (from-to)414-429
Publication StatusPublished
Early online date25/02/15
<mark>Original language</mark>English


Forecasting with longitudinal data has been rarely studied. Most of the available studies are for continuous response and all of them are for univariate response. In this study, we consider forecasting multivariate longitudinal binary data. Five different models including simple ones, univariate and multivariate marginal models, and complex ones, marginally specified models, are studied to forecast such data. Model forecasting abilities are illustrated via a real-life data set and a simulation study. The simulation study includes a model independent data generation to provide a fair environment for model competitions. Independent variables are forecast as well as the dependent ones to mimic the real-life cases best. Several accuracy measures are considered to compare model forecasting abilities. Results show that complex models yield better forecasts.