Functional Adaptive Model Estimation

By Gareth M. James, Bernard Walter Silverman. 2005

120 citations recorded by the source corpus.

Subfields: Genetics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/49a93defa82eeeeccac28e1cea413e044a832eaf

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. H. Muller
2. H. ller
3. Jane-ling Wang
4. Jeng-Min Chiou
5. Yehua Li
6. Raymond K. Wong
7. Fang Yao
8. Han Lin Shang

In Statistics and Probability

1. H. Muller
2. Jeng-Min Chiou
3. Jane-ling Wang
4. Han Lin Shang
5. Yehua Li
6. Raymond K. Wong
7. H. ller
8. Sonja Greven

Citations in the corpus

• Functional additive regression (2015, background): “By Yingying Fan1, Gareth M. James and Peter Radchenko2”
• Functional Modelling and Classification of Longitudinal Data. (2005, methodology): “Any functional regression model that is an extension of a single index or multiple index ( Chiou & Muïler , 2004 ) model such as the models proposed by James & Silverman ( 2005 ) can be extended in the same way to the case of longitudinal data as predictors .”
• Functional Additive Models (2008, methodology): “Ramsay and Silverman (2005) have summarized some of these developments.”
• A survey of functional principal component analysis (2014, background): “1999 , Cardot , Ferraty , Mas & Sarda 2003 , James 2002 , James & Silverman 2005 , Müller & Stadtmüller 2005 , Reiss & Ogden 2007 , Krämer et al .”
• Curve alignment by moments (2007, background): “…and Rice and Wu (2001)], regression with both functional responses [Zeger and Diggle (1994)] and functional predictors [Ferraty and Vieu (2002) and James and Silverman (2005)], functional linear discriminant analysis [James and Hastie (2001) and Ferraty and Vieu (2003)] and functional clustering…”
• Partially Linear Functional Additive Models for Multivariate Functional Data (2018, methodology): “The most widely used approach is to project functional data into a low-rank functional subspace and use the projections as predictors in a nonlinear model (James and Silverman 2005; Li and Hsing 2010a; Yao, Lei, and Wu 2015).”
• 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).”
• The functional linear array model (2015, unknown): “Some approaches model the effect of the functional predictor without the assumption of linearity (e.g., James and Silverman, 2005; Müller et al., 2013; McLean et al., 2014; Zhu et al., 2014).”
• A general framework for functional regression modelling (2017, background): “Then, bxj(xi) = (̂ i1, . . . , ̂iKxj ) and, for example, Pxj = 0 (for an alternative penalizing ˇ away from directions with little variation in x, see James and Silverman (2005)).”
• Functional Data Analysis (2016, methodology): “…are formed by taking into consideration the relation between X and Y , in contrast to other functional regression models, where the predictors are formed merely based on the auto-covariance structure of predictor processes X (James & Silverman 2005; Chen, Hall & M¨uller 2011; Fan et al. 2014).”
• Decision Trees for Functional Variables (2006, background): “Corroborating the conclusions James and other authors reached by independent analyzes on the same dataset (James and Silverman, 2005; Ramsay and Silverman, 2002), the functional splits we learn show that both variability in the y-direction (shrunken bones) and bending to the right are predictive of…”
• Functional Regression (2014, unknown): “Müller & Stadtmüller (2005) introduced functional PCR (fPCR) for GFLM, using smoothed fPC bases for both Xi(t) and B(t) with regularization done by PC truncation.”
• Single and multiple index functional regression models with nonparametric link (2011, unknown): “This class of models has been considered by [15], who referred to them as “Functional Adaptive Models.”
• Penalized Functional Regression (2011, unknown): “…development in regression models where some covariates are functions (James 2002; Cardot, Ferraty, and Sarda 2003; Cardot and Sarda 2005; James and Silverman 2005; Müller 2005; Ramsay and Silverman 2005; Ferraty and Vieu 2006; Reiss and Ogden 2007; Jeff Goldsmith is Ph.D. Candidate…”
• Functional Modelling and Classification of Longitudinal Data * (2005, methodology): “Any functional regression model that is an extension of a single index or multiple index (Chiou & Mu¨ ller, 2004) model such as the models proposed by James & Silverman (2005) can be extended in the same way to the case of longitudinal data as predictors.”