Empirical likelihood and quantile regression in longitudinal data analysis
We propose a novel quantile regression approach for longitudinal data analysis which naturally incorporates auxiliary information from the conditional mean model to account for within-subject correlations. The efficiency gain is quantified theoretically and demonstrated empirically via simulation st...
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| Published in | Biometrika Vol. 98; no. 4; pp. 1001 - 1006 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford University Press for Biometrika Trust
01.12.2011
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| Series | Biometrika |
| Online Access | Get more information |
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| Abstract | We propose a novel quantile regression approach for longitudinal data analysis which naturally incorporates auxiliary information from the conditional mean model to account for within-subject correlations. The efficiency gain is quantified theoretically and demonstrated empirically via simulation studies and the analysis of a real dataset. Copyright 2011, Oxford University Press. |
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| AbstractList | We propose a novel quantile regression approach for longitudinal data analysis which naturally incorporates auxiliary information from the conditional mean model to account for within-subject correlations. The efficiency gain is quantified theoretically and demonstrated empirically via simulation studies and the analysis of a real dataset. Copyright 2011, Oxford University Press. |
| Author | Tang, Cheng Yong Leng, Chenlei |
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