A location-mixture autoregressive model for online forecasting of lung tumor motion

@article{Cervone2014ALA,
  title={A location-mixture autoregressive model for online forecasting of lung tumor motion},
  author={Daniel Cervone and Natesh S. Pillai and Debdeep Pati and Ross Berbeco and John H. Lewis},
  journal={The Annals of Applied Statistics},
  year={2014},
  volume={8},
  pages={1341-1371}
}
  • Daniel Cervone, Natesh S. Pillai, +2 authors John H. Lewis
  • Published 2014
  • Mathematics
  • The Annals of Applied Statistics
  • 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… CONTINUE READING

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