A location-mixture autoregressive model for online forecasting of lung tumor motion
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© 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.
The Annals of Applied Statistics
author list (cited authors)
Cervone, D., Pillai, N. S., Pati, D., Berbeco, R., & Lewis, J. H.
complete list of authors
Cervone, Daniel||Pillai, Natesh S||Pati, Debdeep||Berbeco, Ross||Lewis, John Henry