TIME SERIES ANALYSIS VIA MECHANISTIC MODELS

• 成果类型: Article
• 署名作者: Breto, Carles; He, Daihai; Ionides, Edward L.; King, Aaron A.
• 署名单位: Universidad Carlos III de Madrid; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/08-AOAS201
• 发表日期: 2009
• 页码: 319-348
• 关键词: likelihood-based estimation; sequential monte-carlo; stochastic simulation; parameter-estimation; equation-free; transmission; DYNAMICS; measles; EPIDEMIC; uncertainty

摘要

The purpose of time series analysis via mechanistic models is to reconcile the known or hypothesized structure of a dynamical system with observations collected over time. We develop a framework for Constructing nonlinear mechanistic models and carrying Out inference. Our framework permits the consideration of implicit dynamic models, meaning statistical models for stochastic dynamical systems which are specified by a simulation algorithm to generate sample paths. Inference procedures that operate on implicit models are said to have the plug-and-play property. Our work builds on recently developed plug-and-play inference methodology for partially observed Markov models. We introduce a class of implicitly specified Markov chains with stochastic transition rates, and we demonstrate its applicability to open problems in statistical inference for biological systems. As one example, these models are shown to give if fresh perspective on measles transmission dynamics. As a second example, we present a mechanistic analysis of cholera incidence data, involving interaction between two competing strains of the pathogen Vibrio cholerae.