A study of the partially linear quantile regression model with smooth coefficients
碩士 === 淡江大學 === 統計學系碩士班 === 101 === Quantile regression is a statistical method to analysis the associations between the explanatory variables and the quantile of the response variable. Recently, many researchers have proposed semiparametric quantile regression regression model that have been well d...
| Main Authors: | , |
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2013
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| Online Access: | http://ndltd.ncl.edu.tw/handle/11256733003612776968 |
| Summary: | 碩士 === 淡江大學 === 統計學系碩士班 === 101 === Quantile regression is a statistical method to analysis the associations between the explanatory variables and the quantile of the response variable. Recently, many researchers have proposed semiparametric quantile regression regression model that have been well developed in the context of classical mean regression model. Among the various semiparametric quantile regression model, the partially linear smooth coefficient quantile regression model is the most general and useful quartile regression method. In this dissertation, we shall investigate and compare the kernel estimation methods of Cai and Xiao (2012) and Kai et al. (2011) through a variety of simulation studies. The results show that two kernel estimation methods have similar performances. Cai and Xiao (2012) propose a chi-square test to detect whether a set of coefficients are fixed constant. Their test depends on the number of regression points chosen by the investigator. The simulation studies show that the power performance improves as the number of regression points grows but deteriorates as it grows further. Finally, we provide a chi square table for some significance levels to enable the use of Cai and Xiao’s test.
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