A Record Linkage Approach to Imputation of Missing Data: Analyzing Tag Retention in a Tag: Recapture Experiment
| Autor: | James F. Robison-Cox |
|---|---|
| Rok vydání: | 1998 |
| Předmět: |
Statistics and Probability
Observational error Computer science Applied Mathematics computer.software_genre Missing data Logistic regression Agricultural and Biological Sciences (miscellaneous) Statistics Covariate Data mining Imputation (statistics) Statistics Probability and Uncertainty General Agricultural and Biological Sciences computer Record linkage General Environmental Science |
| Zdroj: | Journal of Agricultural, Biological, and Environmental Statistics. 3:48 |
| ISSN: | 1085-7117 |
| DOI: | 10.2307/1400622 |
| Popis: | Record linkage has been used to combine records in separate files that relate to the same individual or household. In this article, modifications of the typical record linkage algorithm are made for a setting where links are "competitive," that is, a record can be used only once. The goal of the linkage is to recover a missing covariate for use in the logistic regression of tag retention on fish length at time of tagging. The imputation process necessarily adds measurement error to the covariate, biasing logistic regression coefficient estimates toward zero. The extent of the bias is estimated through simulations, and adjustments are made to account for the shrinkage. The data analyzed came from a study of tag retention for visual implant tags in cutthroat trout, where the measurements taken at time of tagging were unavailable for fish that lost the single tag. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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