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On Partial Sufficient Dimension Reduction With Applications to Partially Linear Multi-Index Models
Feng, Zhenghui ; Wen, Xuerong Meggie ; Yu, Zhou ; Zhu, Lixing ; Feng ZH(冯峥晖)
刊名http://dx.doi.org/10.1080/01621459.2012.746065
2013
关键词SLICED INVERSE REGRESSION SINGLE-INDEX MODELS AVERAGE VARIANCE-ESTIMATION CATEGORICAL PREDICTORS ASYMPTOTICS
英文摘要Missouri Research Board; National Natural Science Foundation of China [11201151]; Research Grants Council of Hong Kong; Hong Kong Baptist University; Partial dimension reduction is a general method to seek informative convex combinations of predictors of primary interest, which includes dimension reduction as its special case when the predictors in the remaining part are constants. In this article, we propose a novel method to conduct partial dimension reduction estimation for predictors of primary interest without assuming that the remaining predictors are categorical. To this end, we first take the dichotomization step such that any existing approach for partial dimension reduction estimation can be employed. Then we take the expectation step to integrate over all the dichotomic predictors to identify the partial central subspace. As an example, we use the partially linear multi-index model to illustrate its applications for semiparametric modeling. Simulations and real data examples are given to illustrate our methodology.
语种英语
出版者AMER STATISTICAL ASSOC
内容类型期刊论文
源URL[http://dspace.xmu.edu.cn/handle/2288/90139]  
专题经济学院-已发表论文
推荐引用方式
GB/T 7714
Feng, Zhenghui,Wen, Xuerong Meggie,Yu, Zhou,et al. On Partial Sufficient Dimension Reduction With Applications to Partially Linear Multi-Index Models[J]. http://dx.doi.org/10.1080/01621459.2012.746065,2013.
APA Feng, Zhenghui,Wen, Xuerong Meggie,Yu, Zhou,Zhu, Lixing,&冯峥晖.(2013).On Partial Sufficient Dimension Reduction With Applications to Partially Linear Multi-Index Models.http://dx.doi.org/10.1080/01621459.2012.746065.
MLA Feng, Zhenghui,et al."On Partial Sufficient Dimension Reduction With Applications to Partially Linear Multi-Index Models".http://dx.doi.org/10.1080/01621459.2012.746065 (2013).
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