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Dependent generalized functional linear models

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  • S Jadhav
  • H L Koul
  • Q Lu

Abstract

SummaryThis paper considers testing for no effect of functional covariates on response variables in multivariate regression. We use generalized estimating equations to determine the underlying parameters and establish their joint asymptotic normality. This is then used to test the significance of the effect of predictors on the vector of response variables. Simulations demonstrate the importance of considering existing correlation structures in the data. To explore the effect of treating genetic data as a function, we perform a simulation study using gene sequencing data and find that the performance of our test is comparable to that of another popular method used in sequencing studies. We present simulations to explore the behaviour of our test under varying sample size, cluster size and dimension of the parameter to be estimated, and an application where we are able to confirm known associations between nicotine dependence and neuronal nicotinic acetylcholine receptor subunit genes.

Suggested Citation

  • S Jadhav & H L Koul & Q Lu, 2017. "Dependent generalized functional linear models," Biometrika, Biometrika Trust, vol. 104(4), pages 987-994.
    • Handle: RePEc:oup:biomet:v:104:y:2017:i:4:p:987-994.
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    File URL: http://hdl.handle.net/10.1093/biomet/asx044
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    Cited by:

    1. Jadhav, Sneha & Ma, Shuangge, 2021. "An association test for functional data based on Kendall’s Tau," Journal of Multivariate Analysis, Elsevier, vol. 184(C).

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