Generalized linear models with functional predictors

By Gareth M. James. 2002

452 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5bb9bddce57bdcb528b63a9a84257fdf03921848

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In Statistics and Probability

Citations in the corpus

Functional Causal Mediation Analysis With an Application to Brain Connectivity (2012, background): “A great deal of research has focused on extending linear models to the functional setting (e.g., Hastie and Tibshirani, 1993; Fan and Zhang, 1999; James, 2002; Cardot et al., 2003; M¨uller and StadtM¨uller, 2005).”
Operator-valued Kernels for Learning from Functional Response Data (2015, background): “Regularization can be implemented in a variety of ways, for example by penalized splines (James, 2002) or by truncation of series expansions (Müller, 2005).”
Partially Linear Functional Additive Models for Multivariate Functional Data (2018, background): “…significant amount of recent work devoted to regression models with functional predictors and the most popular model is the functional linear model (James 2002; Cardot, Ferraty, and Sarda 2003; Müller and Stadtmüller 2005; Cai and Hall 2006; Crainiceanu, Staicu, and Di 2009; Li, Wang, and Carroll…”
Functional Modelling and Classification of Longitudinal Data * (2005, background): “Regularization can be implemented in a variety of ways, for example by penalized splines ( James, 2002 ) or by truncation of series expansions (discussed below).”
Single and multiple index functional regression models with nonparametric link (2011, background): “Advances in this direction include those made in [1, 9, 10, 14] and [17], where both parametric and nonparametric link functions were introduced in order to connect the response to a linear functional model in the explanatory variables.”
Prediction in functional linear regression (2006, methodology): “This work was done while Tony Cai was visiting the Mathematical Sciences Institute of the Australian National University.”
Generalized additive models for functional data (2013, methodology): “Our proposal is compared with Functional Generalized Linear Models (James, 2002; Escabias et al., 2004, 2006; Cardot and Sarda, 2005; Müller and Stadtmüller, 2005) and with Generalized Spectral Additive Models (Müller and Yao, 2008) in a simulation study and in a real example using the Rpackage fda.”
Curse of dimensionality and related issues in nonparametric functional regression (2011, unknown): “For a long time, only parametric models were developed for estimating μ in this context, see [24, 25, 26, 5, 9] or [19].”
Using basis expansions for estimating functional PLS regression Applications with chemometric data (2010, methodology): “See [17] for a detailed study of different real-data applications and functional regression models.”
Conditional quantile analysis when covariates are functions, with application to growth data (2012, background): “Such models have been widely studied in the literature (James, 2002; Cardot and Sarda, 2005; Müller and Stadtmüller, 2005; Leng and Müller, 2006; Escabias et al., 2007).”
Functional Modelling and Classification of Longitudinal Data. (2005, background): “Regularization can be implemented in a variety of ways , for example by penalized splines ( James , 2002 ) or by truncation of series expansions ( discussed below ) .”
FUNCTIONAL RESPONSE MODELS (2004, methodology): “A generalized functional model along these lines, allowing for generalized responses, has been proposed by James (2002), who models the principal component functions in a suitable B-spline basis and emphasizes applied aspects.”
Functional Adaptive Model Estimation (2005, methodology): “The third procedure, functional generalized linear models (FGLMs) (James 2002), provides a GLM fit to functional predictors.”
Generalized functional linear models (2005, unknown): “, Ash and Gardner (1975) and Castro, Lawton and Sylvestre (1986)], with the aim to reduce the dimension to the first few components of such an expansion.”
Functional Data Analysis (2016, methodology): “…family or a quasi-likelihood framework and a suitable variance function) This Generalized Functional Linear Model has been considered when g is known (James 2002; Cardot, Ferraty & Sarda 2003; Cardot & Sarda 2005; Wang, Qian & Carroll 2010; Dou, Pollard & Zhou 2012) and when it is unknown (M¨uller…”