Steinsaltz David, Tuljapurkar Shripad, Horvitz Carol
Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, United Kingdom.
Theor Popul Biol. 2011 Aug;80(1):1-15. doi: 10.1016/j.tpb.2011.03.004. Epub 2011 Apr 2.
We consider stochastic matrix models for population driven by random environments which form a Markov chain. The top Lyapunov exponent a, which describes the long-term growth rate, depends smoothly on the demographic parameters (represented as matrix entries) and on the parameters that define the stochastic matrix of the driving Markov chain. The derivatives of a-the "stochastic elasticities"-with respect to changes in the demographic parameters were derived by Tuljapurkar (1990). These results are here extended to a formula for the derivatives with respect to changes in the Markov chain driving the environments. We supplement these formulas with rigorous bounds on computational estimation errors, and with rigorous derivations of both the new and old formulas.
我们考虑由随机环境驱动的种群的随机矩阵模型,这些随机环境构成一个马尔可夫链。描述长期增长率的顶级李雅普诺夫指数α平滑地依赖于人口统计学参数(表示为矩阵元素)以及定义驱动马尔可夫链的随机矩阵的参数。Tuljapurkar(1990)推导了α关于人口统计学参数变化的导数——“随机弹性”。这里将这些结果扩展为关于驱动环境的马尔可夫链变化的导数公式。我们用计算估计误差的严格界限以及新老公式的严格推导来补充这些公式。