Schofield Matthew R, Barker Richard J, Gelling Nicholas
Department of Mathematics and Statistics, University of Otago, New Zealand.
Biometrics. 2018 Jun;74(2):626-635. doi: 10.1111/biom.12763. Epub 2017 Sep 12.
The standard approach to fitting capture-recapture data collected in continuous time involves arbitrarily forcing the data into a series of distinct discrete capture sessions. We show how continuous-time models can be fitted as easily as discrete-time alternatives. The likelihood is factored so that efficient Markov chain Monte Carlo algorithms can be implemented for Bayesian estimation, available online in the R package ctime. We consider goodness-of-fit tests for behavior and heterogeneity effects as well as implementing models that allow for such effects.
拟合连续时间收集的捕获-再捕获数据的标准方法涉及任意地将数据强行纳入一系列不同的离散捕获时段。我们展示了连续时间模型可以像离散时间模型一样轻松拟合。似然函数被分解，以便可以为贝叶斯估计实现高效的马尔可夫链蒙特卡罗算法，可在R包ctime中在线获取。我们考虑了行为和异质性效应的拟合优度检验，以及实现考虑此类效应的模型。
