On the near-singularity of models for animal recovery data.

Abstract:

:Certain probability models sometimes provide poor descriptions when fitted to data by maximum likelihood. We examine one such model for the survival of wild animals, which is fitted to two sets of data. When the model behaves poorly, its expected information matrix, evaluated at the maximum likelihood estimate of parameters, has a 'small' smallest eigenvalue. This is due to the fitted model being similar to a parameter-redundant submodel. In this case, model parameters that are precisely estimated have small coefficients in the eigenvector corresponding to the smallest eigenvalue. Approximate algebraic expressions are provided for the smallest eigenvalue. We discuss the general applicability of these results.

journal_name

Biometrics

journal_title

Biometrics

authors

Catchpole EA,Kgosi PM,Morgan BJ

doi

10.1111/j.0006-341x.2001.00720.x

subject

Has Abstract

pub_date

2001-09-01 00:00:00

pages

720-6

issue

3

eissn

0006-341X

issn

1541-0420

journal_volume

57

pub_type

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