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Stocahastic process based regression modeling of time-to-event data : application to phenological data

University of British Columbia

In agricultural study, the timings of phenological events, such as bud-bursting, blooming and fruiting, are considered to be mainly influenced by climate variables, especially accumulative daily average temperatures. We developed a stochastic process-based regression model to study the complicated relationship between phenological events and climate variables, and to predict the future phenological events. Compared with the traditional Cox model, the newly developed model is more efficient by using all available time-dependent covariate information, and is suitable for making predictions. Compared with parametric proportional hazards model, this model is less restrictive on assumptions, and fitting of this model to data is computationally straightforward. Also, this model may be easily extended to incorporate sequential events as responses. It may also be useful for a broad range of survival data in medical study. This model was applied to the bloom-date data of six high-valued, woody perennial crops in the Okanagan Valley, BC Canada. Simulation results showed that the model provides a sensible way to estimate an important parameter, Tbase, controlling phenological forcing events. Also, our statistical findings support Scientists' previous experimental findings that the temperature influence blooming events via accumulation of growing degree days (GDDs). Furthermore, a cross-validation procedure showed that this model can provide accurate predictions for future blooming events.

Text

2010-08-23

Attribution-NonCommercial-NoDerivs 3.0 Unported

http://hdl.handle.net/2429/27645

Statistics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/3.0/