An adaptive estimation of MAVE

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Show simple item record Wang, Qin Yao, Weixin 2012-05-24T16:23:00Z 2012-05-24T16:23:00Z 2012-05-24
dc.description.abstract Minimum average variance estimation (MAVE, Xia et al: 2002) is an effective dimension reduction method. It requires no strong probabilistic assumptions on the predictors, and can consistently estimate the central mean subspace. It is applicable to a wide range of models, including time series. However, the least squares criterion used in MAVE will lose its effciency when the error is not normally distributed. In this article, we propose an adaptive MAVE which can be adaptive to different error distributions. We show that the proposed estimate has the same convergence rate as the original MAVE. An EM algorithm is proposed to implement the new adaptive MAVE. Using both simulation studies and a real data analysis, we demonstrate the superior finite sample performance of the proposed approach over the existing least squares based MAVE when the error distribution is non-normal and comparable performance when the error is normal. en_US
dc.relation.uri en_US
dc.subject Sufficient dimension reduction en_US
dc.subject Central mean subspace en_US
dc.subject MAVE en_US
dc.subject Adaptive estimation en_US
dc.title An adaptive estimation of MAVE en_US
dc.type Article (author version) en_US 2012 en_US
dc.citation.doi doi:10.1016/j.jmva.2011.07.001 en_US
dc.citation.epage 100 en_US
dc.citation.issue 1 en_US
dc.citation.jtitle Journal of Multivariate Analysis en_US
dc.citation.spage 88 en_US
dc.citation.volume 104 en_US
dc.contributor.authoreid wxyao en_US

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