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Testing of Goodness of Fit in Semiparametric Logistic Regression Models of Correlated Binary Data



In this paper we consider the performance of two methods of goodness of fit test for logistic regression when the model is semiparametric and the data are correlated. These tests are based on residual, standardized and unstandardized residual where originally are designed for parametric models. We extend those ones to semiparametric model. Estimation of the model uses GEE-Smoothing Spline, where the basic of estimation is GEE and the estimation of nonparametric component is based on smoothing spline. We obtained that the methods have good performance to detect correct models, but very poor to detect incorect semiparametric models. We conjecture that it is caused by fact that the nonparametric component in the semiparametric model will automatically adjust incorrect model, such that the estimated model close the true model.


Longitudinal binary data, Semiparametric model, Generalized estimating equation, Semiparametric Logistic GEE, Goodness of fits, standardized and unstandardized residual.

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