Robust variable selection through MAVE

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dc.contributor.author Yao, Weixin
dc.contributor.author Wang, Qin
dc.date.accessioned 2013-05-22T19:57:59Z
dc.date.available 2013-05-22T19:57:59Z
dc.date.issued 2013-05-22
dc.identifier.uri http://hdl.handle.net/2097/15845
dc.description.abstract Dimension reduction and variable selection play important roles in high dimensional data analysis. The sparse MAVE, a model-free variable selection method, is a nice combination of shrinkage estimation, Lasso, and an effective dimension reduction method, MAVE (minimum average variance estimation). However, it is not robust to outliers in the dependent variable because of the use of least-squares criterion. A robust variable selection method based on sparse MAVE is developed, together with an efficient estimation algorithm to enhance its practical applicability. In addition, a robust cross-validation is also proposed to select the structural dimension. The effectiveness of the new approach is verified through simulation studies and a real data analysis. en_US
dc.language.iso en_US en_US
dc.relation.uri http://www.sciencedirect.com/science/article/pii/S0167947313000364 en_US
dc.subject Sufficient dimension reduction en_US
dc.subject MAVE en_US
dc.subject Shrinkage estimation en_US
dc.subject Robust estimation en_US
dc.title Robust variable selection through MAVE en_US
dc.type Article (author version) en_US
dc.date.published 2013 en_US
dc.citation.doi doi:10.1016/j.csda.2013.01.021 en_US
dc.citation.epage 49 en_US
dc.citation.jtitle Computational Statistics and Data Analysis en_US
dc.citation.spage 42 en_US
dc.citation.volume 63 en_US
dc.contributor.authoreid wxyao en_US


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