Abstract
A normal semiparametric mixture regression model is proposed for longitudinal data. The proposed model contains one smooth term and a set of possible linear predictors. Model terms are estimated using the penalized likelihood method with the EM algorithm. A computationally feasible alternative method that provides an approximate solution is also introduced. Simulation experiments and a real data example are used to illustrate the methods.
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Nummi, T., Salonen, J., Koskinen, L. et al. A semiparametric mixture regression model for longitudinal data. J Stat Theory Pract 12, 12–22 (2018). https://doi.org/10.1080/15598608.2017.1298062
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DOI: https://doi.org/10.1080/15598608.2017.1298062