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Accession Number 5004893

Title Hierarchical models for estimation of population parameters

Project Description Hierarchical models for estimation of population parameters Project status: Active Start date:

011001 End date: 061001 Wildlife management and ecological research are increasingly guided

by analyses of large and complex datasets. The description of such datasets often requires a

large number of parameters, among which certain patterns might be discernible. For example, one

may consider a long-term study producing estimates of annual survival rates; of interest is the

question whether these rates have declined through time. Several statistical methods exist for

examining pattern in collections of parameters. Random effects models, in which parameters are

regarded as random variables, with distributions governed by "hyperparameters" are required for

describing the patterns of interest. Unfortunately, implementation of random effects models is

sometimes difficult. Ultrastructural models, in which the postulated pattern is built into the

parameter structure of the original data analysis, are approximations to random effects models.

However, this approximation is not completely satisfactory: failure to account for natural variation

among parameters can lead to overstatement of the evidence for pattern among parameters.

Quasi-likelihood methods can be used to improve the approximation of random effects models by

ultrastructural models, but frequently lack a firm mathematical foundation and hence produce

inferences of dubious merit. This study investigates the use of Bayesian hierarchical models, and

Markov Chain Monte Carlo methods, to a wide variety of wildlife and ecological problems, including

capture-recapture models and demographic analysis.

Keywords bayesian inference, estimation, hierarchical modeling, markov chain monte carlo,

Principal William A Link, USGS Patuxent Wildlife Research Center:;


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