A Corrected Plug-in Method for the Quantile Confidence Interval of a Transformed Regression
In this paper we propose an analytically corrected plug-in method for constructing confidence intervals of the conditional quantiles of a response variable with data transformation. The method can be applied to (i) a general conditional regression quantile, (ii) a general monotonic transformation, a...
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| Main Authors: | , |
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| 格式: | text |
| 語言: | English |
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Institutional Knowledge at Singapore Management University
2002
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| 在線閱讀: | https://ink.library.smu.edu.sg/soe_research/697 https://ink.library.smu.edu.sg/context/soe_research/article/1696/viewcontent/Yang_Tse.pdf |
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| 總結: | In this paper we propose an analytically corrected plug-in method for constructing confidence intervals of the conditional quantiles of a response variable with data transformation. The method can be applied to (i) a general conditional regression quantile, (ii) a general monotonic transformation, and (iii) a transformation model with heteroscedastic errors. Our results extend those in Yang (2002a), in which the median of a response variable under the Box-Cox transformation with homoscedastic errors was considered. A Monte Carlo experiment is conducted to compare the performance of the corrected plug-in method, the plug-in method and the delta method. The corrected plug-in method provides superior results over the other two methods. |
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