A location-mixture autoregressive model for online forecasting of lung tumor motion Academic Article uri icon


  • Institute of Mathematical Statistics, 2014. Lung tumor tracking for radiotherapy requires real-time, multiple-step ahead forecasting of a quasi-periodic time series recording instantaneous tumor locations. We introduce a location-mixture autoregressive (LMAR) process that admits multimodal conditional distributions, fast approximate inference using the EM algorithm and accurate multiple-step ahead predictive distributions. LMAR outperforms several commonly used methods in terms of out-of-sample prediction accuracy using clinical data from lung tumor patients. With its superior predictive performance and real-time computation, the LMAR model could be effectively implemented for use in current tumor tracking systems.

published proceedings

  • The Annals of Applied Statistics

altmetric score

  • 0.25

author list (cited authors)

  • Cervone, D., Pillai, N. S., Pati, D., Berbeco, R., & Lewis, J. H.

citation count

  • 4

complete list of authors

  • Cervone, Daniel||Pillai, Natesh S||Pati, Debdeep||Berbeco, Ross||Lewis, John Henry

publication date

  • January 2014