| Empirical likelihood-based inference in a partially linear model for longitudinal data |
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| 基金项目： | The first author was supported by the National Natural Science Foundation of China (Grant No. 10571008)，the Natural Science Foundation of Beijing (Grant No. 1072004)，the Science and Technology Development Project of Education Committee of Beijing City (Grant No. KM200510005009)，The second author was supported by a grant of the Research Grant Council of Hong Kong (Grant No. HKBU7060/04P) |
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| 摘 要： | A partially linear model with longitudinal data is considered, empirical likelihood to infer- ence for the regression coefficients and the baseline function is investigated, the empirical log-likelihood ratios is proven to be asymptotically chi-squared, and the corresponding confidence regions for the pa- rameters of interest are then constructed. Also by the empirical likelihood ratio functions, we can obtain the maximum empirical likelihood estimates of the regression coefficients and the baseline function, and prove the asymptotic normality. The numerical results are conducted to compare the performance of the empirical likelihood and the normal approximation-based method, and a real example is analysed.
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| 收稿时间： | 13 September 2005 |
| 修稿时间： | 28 July 2006 |
Empirical likelihood-based inference in a partially linear model for longitudinal data |
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| Authors: | Xue LiuGen Zhu LiXing |
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| Affiliation: | (1) College of Applied Sciences, Beijing University of Technology, Beijing, 100022, China;(2) Department of Mathematics, Hong Kong Baptist University, Hong Kong, China |
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| Abstract: | A partially linear model with longitudinal data is considered, empirical likelihood to infer- ence for the regression coefficients and the baseline function is investigated, the empirical log-likelihood ratios is proven to be asymptotically chi-squared, and the corresponding confidence regions for the pa- rameters of interest are then constructed. Also by the empirical likelihood ratio functions, we can obtain the maximum empirical likelihood estimates of the regression coefficients and the baseline function, and prove the asymptotic normality. The numerical results are conducted to compare the performance of the empirical likelihood and the normal approximation-based method, and a real example is analysed. |
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| Keywords: | partially linear model empirical likelihood confidence region longitudinal data |
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