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Semiparametric Approach to Logistic Model with Random Intercept
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 Title & Authors
Semiparametric Approach to Logistic Model with Random Intercept
Kim, Mijeong;
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 Abstract
Logistic models with a random intercept are useful to analyze longitudinal binary data. Traditionally, the random intercept of the logistic model is assumed to be parametric (such as normal distribution) and is also assumed to be independent to variables. Such assumptions are very strong and restricted for application to real data. Recently, Garcia and Ma (2015) derived semiparametric efficient estimators for logistic model with a random intercept without these assumptions. Their estimator shows the consistency where we do not assume any parametric form for the random intercept. In addition, the method is computationally simple. In this paper, we apply this method to analyze toenail infection data. We compare the semiparametric estimator with maximum likelihood estimator, penalized quasi-likelihood estimator and hierarchical generalized linear estimator.
 Keywords
semiparametric method;logistic model;random intercept;longitudinal data;
 Language
Korean
 Cited by
 References
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