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Modeling pattern in collections of parameters

Journal of Wildlife Management

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Abstract

Wildlife management is 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. Here, I argue for the superiority of 'random effects models' in which parameters are regarded as random variables, with distributions governed by 'hyperparameters' 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. I describe quasi-likelihood methods that can be used to improve the approximation of random effects models by ultrastructural models.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Modeling pattern in collections of parameters
Series title:
Journal of Wildlife Management
Volume
63
Issue:
3
Year Published:
1999
Language:
English
Contributing office(s):
Patuxent Wildlife Research Center
Description:
1017-1027
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
First page:
1017
Last page:
1027
Number of Pages:
11