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Heterogeneous Capture–Recapture Models with Covariates: A Partial Likelihood Approach for Closed Populations
Author(s) -
Stoklosa Jakub,
Hwang WenHan,
Wu ShengHai,
Huggins Richard
Publication year - 2011
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2011.01596.x
Subject(s) - mark and recapture , covariate , extrapolation , computer science , mixed model , generalized linear mixed model , contrast (vision) , software , marginal likelihood , statistics , econometrics , maximum likelihood , data mining , machine learning , mathematics , artificial intelligence , population , demography , sociology , programming language
Summary In practice, when analyzing data from a capture–recapture experiment it is tempting to apply modern advanced statistical methods to the observed capture histories. However, unless the analysis takes into account that the data have only been collected from individuals who have been captured at least once, the results may be biased. Without the development of new software packages, methods such as generalized additive models, generalized linear mixed models, and simulation–extrapolation cannot be readily implemented. In contrast, the partial likelihood approach allows the analysis of a capture–recapture experiment to be conducted using commonly available software. Here we examine the efficiency of this approach and apply it to several data sets.