Estimation in generalized linear models for functional data via penalized likelihood

By Hervé Cardot, Pascal Sarda. 2005

167 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4da976bee9bae52df4ba11a8e0dd49aa4b856b25

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In Statistics and Probability

Citations in the corpus

Continuously additive models for nonlinear functional regression (2013, methodology): “Biometrika (2013), 100, 3, pp. 607–622 doi: 10.1093/biomet/ast004 C© 2013 Biometrika Trust Advance Access publication 26 March 2013 Printed in Great Britain Continuously additive models for nonlinear functional regression”
DECIDING THE DIMENSION OF EFFECTIVE DIMENSION REDUCTION SPACE FOR FUNCTIONAL AND HIGH-DIMENSIONAL DATA (2010, background): “…for linear models, see Cardot, Ferraty and Sarda (2003), Ramsay and Silverman (2005), Cai and Hall (2006) and Crambes, Kneip and Sarda (2009); for nonlinear models, see Hastie and Tibshirani (1990), Cardot and Sarda (2005), James and Silverman (2005) and Müller and Stadtmüller (2005).”
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.”
Shrinkage estimation and selection for multiple functional regression (2011, background): “How to extend the shrinkage estimation results to generalized functional linear model (James, 2002; Muller and Stadtmuller, 2005; Cardot and Sarda, 2005) is another interesting topic for further study.”
Partially functional linear regression in high dimensions (2016, unknown): “Functional linear models have been extended to generalized functional linear models (Escabias et al., 2004; Cardot & Sarda, 2005; Müller & Stadtmüller, 2005), varying-coefficient c© 2016 Biometrika Trust D ow nloaded from https://academ ic.oup.com /biom et/article/103/1/147/2389939 by Peking U…”
Functional Additive Models (2008, background): “These include generalized functional linear models (James 2002; Escabias, Aguilera, and Valderrama 2004; Cardot and Sarda 2005; Müller and Stadtmüller 2005); modifications of functional regression for longitudinal, (i.”
Functional linear regression via canonical analysis (2010, methodology): “The functional regression model (2) for the case of scalar responses has attracted much recent interest (Cardot and Sarda (2005); Müller and Stadtmüller (2005); Hall and Horowitz (2007)), while the case of functional responses has been much less thoroughly investigated (Ramsay and Dalzell (1991); Ya”
Using basis expansions for estimating functional PLS regression Applications with chemometric data (2010, methodology): “One standard method to estimate functional regression models is to use a roughness penalty approach thatworkswellwith noisy or unequally spaced observations of thecurves [7,21].”
Penalized Functional Regression (2011, unknown): “The generalized functional linear model relating Yi to the covariates Xi(t),Zi is given by (McCullagh and Nelder 1989; Cardot and Sarda 2005; Müller and Stadtmüller 2005)”
Functional Data Analysis (2016, background): “…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 & Stadtm¨uller 2005; Chen, Hall & M¨uller…”
On rates of convergence in functional linear regression (2007, background): “[4], Cardot and Sarda [5], Hall and Horowitz [11] and Müller and Stadtmüller [14].”
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.”