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Semiparametric Regression Analysis of Longitudinal Skewed Data
Author(s) -
Lin Huazhen,
Zhou Ling,
Zhou Xiaohua
Publication year - 2014
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/sjos.12080
Subject(s) - semiparametric regression , mathematics , estimator , semiparametric model , econometrics , statistics , regression analysis , multiplicative function , parametric statistics , function (biology) , regression , transformation (genetics) , mathematical analysis , evolutionary biology , biology , biochemistry , chemistry , gene
In this paper, we develop a semiparametric regression model for longitudinal skewed data. In the new model, we allow the transformation function and the baseline function to be unknown. The proposed model can provide a much broader class of models than the existing additive and multiplicative models. Our estimators for regression parameters, transformation function and baseline function are asymptotically normal. Particularly, the estimator for the transformation function converges to its true value at the rate n − 1 ∕ 2 , the convergence rate that one could expect for a parametric model. In simulation studies, we demonstrate that the proposed semiparametric method is robust with little loss of efficiency. Finally, we apply the new method to a study on longitudinal health care costs.