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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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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.
semiparametric method;logistic model;random intercept;longitudinal data;
 Cited by
Bickel, P. J., Klaassen, C. A. J., Ritov, Y. and Wellner, J. A. (1993). Efficient and Adaptive Estimation for Semiparametric Models, The Johns Hopkins University Press, Baltimore.

Breslow, N. E. and Clayton, D. G. (1993). Approximate inference in generalized linear mixed models, Journal of the American Statistical Association, 88, 9-25.

Garcia, T. P. and Ma, Y. (2015). Optimal estimator for logistic model with distribution-free random intercept, Scandinavian Journal of Statistics, in press.

Hausman, J. A. (1978). Specification tests in econometrics, Econometrica, 46, 1251-1271. crossref(new window)

Jang, W. and Lim, J. (2006). PQL estimation biases in generalized linear mixed models, Institute of Statistics and Decision Sciences, Duke University Springer-Verlag, Durham, NC, USA, 5-21.

Newey, W. and Powell, J. L. (1990). Efficient estimation of linear and type I censored regression models under conditional quantile restrictions, Econometric Theory, 6, 295-317. crossref(new window)

Neumann, C. G., Bwibo, N. O., Murphy, S. P., Sigman, M., Guthrie, D., Weiss, R. E., Allen, L. H. and Demment, M. W. (2003). Animal source foods improve dietary quality, micronutrient status, growth and cognitive function in Kenyan school children: background, study design and baseline findings, The Journal of Nutrition, 133, 3941S-3949S. crossref(new window)

Neumann, C. G., Bwibo, N. O., Jiang, L. and Weiss, R. E. (2013). School snacks decrease morbidity in Kenyan schoolchildren: A cluster randomized, controlled feeding intervention trial, Public Health Nutrition, 16, 1593-1604. crossref(new window)

Raudenbush, S. W. and Bryk, A. S. (2002). Hierarchical Linear Models, 2nd Ed., Sage Publications, California.

Schall, R. (1991). Estimation in generalized linear models with random effects, Biometrika, 78, 719-727. crossref(new window)

Skrondal, A. and Rabe-Hesketh, S. (2009). Prediction in multilevel generalized linear models, Journal of the Royal Statistical Society: Series A (Statistics in Society), 172, 659-687. crossref(new window)

Tsiatis, A. A. (2006). Semiparametric Theory and Missing Data, Springer, New York.

Weiss, R. E. (2005). Modeling Longitudinal Data, Springer-Verlag, New York.