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Functional Generalized Linear Models with Images as Predictors
Biometrics (2010)
  • Philip T. Reiss, New York University
  • R. Todd Ogden, Columbia University

Functional principal component regression (FPCR) is a promising new method for regressing scalar outcomes on functional predictors. In this paper we present a theoretical justification for the use of principal components in functional regression. FPCR is then extended in two directions: from linear to the generalized linear modeling, and from univariate signal predictors to high-resolution image predictors. We show how to implement the method efficiently by adapting generalized additive model technology to the functional regression context. A technique is proposed for estimating simultaneous confidence bands for the coefficient function; in the neuroimaging setting, this yields a novel means to identify brain regions that are associated with a clinical outcome. A novel application of likelihood ratio testing is described for assessing the null hypothesis of a constant coefficient function. The performance of the methodology is illustrated via simulations and real data analyses with positron emission tomography images as predictors.

  • B-splines,
  • Functional principal component regression,
  • Positron emission tomography,
  • Simultaneous confidence bands,
  • Smoothing parameter
Publication Date
March, 2010
Citation Information
Philip T. Reiss and R. Todd Ogden. "Functional Generalized Linear Models with Images as Predictors" Biometrics Vol. 66 Iss. 1 (2010)
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