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Bootstrap and Asymptotic Prediction Criterion Estimates for Binomial Proportions in Insemination Data
Author(s) -
Bonneu M.,
Lavergne C.
Publication year - 1992
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.4710340107
Subject(s) - akaike information criterion , statistics , mathematics , bayesian information criterion , logistic regression , econometrics , information criteria , standard error , model selection
Model choice techniques are proposed for logistic regression, based on prediction criterion estimation similar to Akaike's information criterion. For artificial insemination data of cattle, we wish to study a factor influence on success proportion; tests standard methods don't always seem suitable for prediction objective. Two prediction criterion estimate methods are applied to these data: simulated bootstrap and asymptotic estimates. Some empirical properties of this estimate are studied.