标题： 面板数据的机制模型：四种相互作用物种的生态实验分析 显示英文标题

标题： Mechanistic models for panel data: Analysis of ecological experiments with four interacting species

摘要： 在生态学背景下，当对一组生态过程进行时间序列测量时，就会产生面板数据。每个过程可能对应于野外数据的空间位置，或者对应于设计实验中的实验生态系统。生态面板数据的统计模型应该捕捉到生态系统的非线性、随机性和测量不确定性等固有特征。此外，系统动力学可能依赖于不可观测的变量。本研究应用迭代粒子滤波技术，探索基于似然的这些复杂系统的统计分析的新可能性。我们分析了来自一个中宇宙实验的数据，在这个实验中，淡水浮游甲壳纲动物属 Daphnia 的两个物种与一种藻类和一种真菌寄生虫共存。在六种处理条件下，对复制的中宇宙进行了时间序列数据的收集。迭代滤波能够最大化科学动机驱动的非线性部分可观测马尔可夫过程模型的似然函数，从而提供对参数估计、置信区间、假设检验、模型选择和诊断的标准似然方法。这一工具箱允许科学家提出并评估受科学启发的随机动态模型用于面板数据，唯一的要求是编写从模型模拟的代码以及指定描述系统状态如何被观测的测量分布。 显示英文摘要

摘要： In an ecological context, panel data arise when time series measurements are made on a collection of ecological processes. Each process may correspond to a spatial location for field data, or to an experimental ecosystem in a designed experiment. Statistical models for ecological panel data should capture the high levels of nonlinearity, stochasticity, and measurement uncertainty inherent in ecological systems. Furthermore, the system dynamics may depend on unobservable variables. This study applies iterated particle filtering techniques to explore new possibilities for likelihood-based statistical analysis of these complex systems. We analyze data from a mesocosm experiment in which two species of the freshwater planktonic crustacean genus, Daphnia, coexist with an alga and a fungal parasite. Time series data were collected on replicated mesocosms under six treatment conditions. Iterated filtering enables maximization of the likelihood for scientifically motivated nonlinear partially observed Markov process models, providing access to standard likelihood-based methods for parameter estimation, confidence intervals, hypothesis testing, model selection and diagnostics. This toolbox allows scientists to propose and evaluate scientifically motivated stochastic dynamic models for panel data, constrained only by the requirement to write code to simulate from the model and to specify a measurement distribution describing how the system state is observed.