A new orthogonality-based estimation for varying-coefficient partially linear models |
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Authors: | Peixin Zhao Yiping Yang |
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Institution: | 1. College of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing 400067, China;2. Chongqing key laboratory of social economy and applied statistics, Chongqing 400067, China |
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Abstract: | Varying coefficient partially linear models are usually used for longitudinal data analysis, and an interest is mainly to improve efficiency of regression coefficients. By the orthogonality estimation technology and the quadratic inference function method, we propose a new orthogonality-based estimation method to estimate parameter and nonparametric components in varying coefficient partially linear models with longitudinal data. The proposed procedure can separately estimate the parametric and nonparametric components, and the resulting estimators do not affect each other. Under some mild conditions, we establish some asymptotic properties of the resulting estimators. Furthermore, the finite sample performance of the proposed procedure is assessed by some simulation experiments. |
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Keywords: | primary 62G05 secondary 62G20 Varying coefficient partially linear model Longitudinal data QR decomposition Quadratic inference function |
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