首页 | 本学科首页   官方微博 | 高级检索  
     检索      


Using hidden Markov chains and empirical Bayes change-point estimation for transect data
Authors:JAY M VERHOEF  NOEL CRESSIE
Institution:(1) Alaska Department of Fish and Game, 1300 College Road, Fairbanks, Alaska 99701, USA;(2) Department of Statistics, Iowa State University, Ames, Iowa 50011, USA
Abstract:Consider a lattice of locations in one dimension at which data are observed. We model the data as a random hierarchical process. The hidden process is assumed to have a (prior) distribution that is derived from a two-state Markov chain. The states correspond to the mean values (high and low) of the observed data. Conditional on the states, the observations are modelled, for example, as independent Gaussian random variables with identical variances. In this model, there are four free parameters: the Gaussian variance, the high and low mean values, and the transition probability in the Markov chain. A parametric empirical Bayes approach requires estimation of these four parameters from the marginal (unconditional) distribution of the data and we use the EM-algorithm to do this. From the posterior of the hidden process, we use simulated annealing to find the maximum a posteriori (MAP) estimate. Using a Gibbs sampler, we also obtain the maximum marginal posterior probability (MMPP) estimate of the hidden process. We use these methods to determine where change-points occur in spatial transects through grassland vegetation, a problem of considerable interest to plant ecologists.
Keywords:spatial statistics  image analysis  EM-algorithm  simulated annealing  Gibbs sampler
本文献已被 SpringerLink 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号