Discrete-Time and Continuous-Time Models for Ecological Monitoring Data

Abstract

Discrete-Time and Continuous-Time Models for Ecological Monitoring Data Jun Zhu Dept. of Statistics and Entomology, University of Wisconsin at Madison, USA ?: Both discrete-time and continuous-time models can be used for the analysis of environmental or ecological monitoring data such that subjects are observed at multiple monitoring time points across space. In this talk, these two modeling approaches will be explored. Of particular interest are additive hazards regression models where the baseline hazard function can take on flexible forms.? Time-varying covariates are considered and spatial dependence is taken into account via autoregression in space and time. Statistical inference is developed for the regression coefficients via partial likelihood. Asymptotic properties, including consistency and asymptotic normality, are established for parameter estimates under suitable regularity conditions. Feasible algorithms utilizing existing statistical software packages are developed for computation. A simulation study demonstrates that the statistical inference using partial likelihood has sound finite-sample properties and offers a viable alternative to maximum likelihood estimation. For illustration, data from an ecological study that monitors bark beetle colonization of red pines in a plantation of Wisconsin are analyzed.