Mathematics – Statistics Theory
Scientific paper
2008-01-31
Annals of Applied Statistics 2009, Vol. 3, No. 1, 319-348
Mathematics
Statistics Theory
Published in at http://dx.doi.org/10.1214/08-AOAS201 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Ins
Scientific paper
10.1214/08-AOAS201
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 a 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.
Bretó Carles
He Daihai
Ionides Edward L.
King Aaron A.
No associations
LandOfFree
Time series analysis via mechanistic models does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Time series analysis via mechanistic models, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Time series analysis via mechanistic models will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-37359