Continuous-Time Proportional Hazards Regression for Ecological Monitoring Data.

ABSTRACT: We consider a continuous-time proportional hazards model for the analysis of ecological monitoring data where subjects are monitored at discrete times and fixed sites across space. Since the exact time of event occurrence is not directly observed, we rely on dichotomous event indicators observed at monitoring times to make inference about the model parameters. We use autoregression on the response at neighboring sites from a previous time point to take into account spatial dependence. The interesting fact is utilized that the probability of observing an event at a monitoring time when the underlying hazards is proportional falls under the class of generalized linear models with binary responses and complementary log-log link functions. Thus, a maximum likelihood approach can be taken for i

SUBMITTER: Lin FC 

PROVIDER: S-EPMC3849820 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature