Tests for High Dimensional Generalized Linear Models

Song Chen and Bin Guo

MPRA Paper from University Library of Munich, Germany

Abstract: We consider testing regression coefficients in high dimensional generalized linear models. By modifying a test statistic proposed by Goeman et al. (2011) for large but fixed dimensional settings, we propose a new test which is applicable for diverging dimension and is robust for a wide range of link functions. The power properties of the tests are evaluated under the setting of the local and fixed alternatives. A test in the presence of nuisance parameters is also proposed. The proposed tests can provide p-values for testing significance of multiple gene-sets, whose usefulness is demonstrated in a case study on an acute lymphoblastic leukemia dataset.

Keywords: Generalized Linear Model; Gene-Sets; High Dimensional Covariate; Nuisance Parameter; U-statistics. (search for similar items in EconPapers)
JEL-codes: C3 C30 C4 C5 (search for similar items in EconPapers)
Date: 2014
New Economics Papers: this item is included in nep-ecm
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Related works:
Journal Article: Tests for high dimensional generalized linear models (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:59816

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