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Semiparametric Estimation of Tag Loss and Reporting Rates for Tag‐Recovery Experiments Using Exact Time‐at‐Liberty Data
Author(s) -
Cadigan N. G.,
Brattey J.
Publication year - 2003
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.0006-341x.2003.00101.x
Subject(s) - nonparametric statistics , parametric statistics , statistics , econometrics , computer science , population , estimation , smoothing , mathematics , medicine , environmental health , management , economics
Summary . We present a semiparametric likelihood approach to estimating reporting rates and tag‐loss rates from the tags returned from capture‐recapture studies. Such studies are commonly used to estimate critical population parameters. Tag loss rates are estimated using double‐tagged animals, while reporting rates are estimated using information from high‐reward tags. A likelihood function is constructed based on the conditional distribution of the type of tag returned (low or high reward, single or double tag), given that a tag has been returned. This involves many sparse 5 × 1 tag‐return contingency tables, and choosing a good functional form for the tag loss rate is difficult with such data. We model tag‐loss rates using monotone‐smoothing splines, and use these nonparametric estimates to diagnose the parametric form of the tag‐loss rate. The nonparametric methods can also be used directly to model tag‐loss rates.