Tests for High Dimensional Generalized Linear Models
Song Chen and
MPRA Paper from University Library of Munich, Germany
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)
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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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