An Adaptive Estimation Method for Semiparametric Models and Dimension Reduction
نویسندگان
چکیده
Xia, Tong, Li and Zhu (2002) proposed a general estimation method termed minimum average variance estimation (MAVE) for semiparametric models. The method has been found very useful in estimating complicated semiparametric models (Xia, Zhang and Tong, 2004; Xia and Härdle, 2006) and general dimension reduction (Xia, 2008; Wang and Xia, 2008). The method is also convenient to combine with other methods in order to incorporate additional statistical requirements (Wang and Yin, 2007). In this paper, we give a general review on the method and discuss some issues arising in estimating semiparametric models and dimension reduction (Li, 1991 and Cook, 1998) when complicated statistical requirements are imposed, including quantile regression, sparsity of variables and censored data.
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My core statistical research focuses on semiparametric/nonparametric methodology and large sample theory — efficient estimation in semiparametric models, nonparametric function estimation (with emphasis on shape constrained estimation), and bootstrap based inference in non-standard problem. I am also actively involved in interdisciplinary research in astronomy. My research has applications in b...
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