Functional Additive Models

By H. Muller, Fang Yao. 2008

277 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3c7d33f8d1f3450657a1de2e5dd56f2b35db658e

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In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Weakly dependent functional data (2010, unknown): “The Annals of Statistics 2010, Vol. 38, No. 3, 1845–1884 DOI: 10.1214/09-AOS768 © Institute of Mathematical Statistics, 2010”
Functional additive regression (2015, background): “By Yingying Fan1, Gareth M. James and Peter Radchenko2”
Additive Functional Regression for Densities as Responses (2020, methodology): “…procedures can be implemented with vectorized versions of the predictor function, such as functional principal components, extending the additive model for the case where both predictors and responses are functional (see, e.g., Müller and Yao 2008) to the case where responses are density functions.”
A survey of functional principal component analysis (2014, methodology): “) functional additive models , where the response is scalar and predictor is function ( see for example , Müller & Yao 2008 , 2010 , Febrero - Bande & González - Manteiga 2011 , Ferraty et al .”
Continuously additive models for nonlinear functional regression (2013, methodology): “…nonparametric functional models (Ferraty & Vieu, 2006) and functional additive models where centred predictor functions are projected on eigenfunctions of the predictor process and the model is then assumed to be additive in the resulting functional principal components (Müller & Yao, 2008).”
Properties of design-based functional principal components analysis (2008, background): “Chiou et al. (2003) proposed nonparametric estimators of function fj based on single index models that could explain the principal components scores thanks to real covariates whereas Müller and Yao (2008) consider additive models.”
Locally Stationary Functional Time Series (2016, background): “For p “ 2, the proposition yields a time-varying version of the corresponding result of Panaretos and Tavakoli (2013a). The more general case p “ 8 also includes linear models introduced by Bosq (2000) and Hörmann and Kokoszka (2010) as well as the important class of time-varying functional autoregr”
Partially Linear Functional Additive Models for Multivariate Functional Data (2018, methodology): “These results agree with the general belief that irrigation and precipitation are informative in yield prediction, which also stress the importance of extending the FAM of Müller and Yao (2008) to our PLFAM.”
Functional Data Analysis (2016, background): “This implies that simple one-dimensional smoothing of the responses against the FPCs leads to consistent estimates of the component functions f k (M¨uller & Yao 2008), so that the usual backﬁtting that is normally required for additive modeling is not needed.”
Functional Principal Component Regression and Functional Partial Least‐squares Regression: An Overview and a Comparative Study (2017, methodology): “…and Yao, Müller and Wang (2005), generalized functional linear models as in Müller and Stadtmüller (2005), additive functional linear models as in Müller and Yao (2008) and Fan, James and Radchenko (2015) and generalized additive functional models as in Febrero-Bande and González-Manteiga…”
Partially functional linear regression in high dimensions (2016, methodology): “…ic.oup.com /biom et/article/103/1/147/2389939 by Peking U niversity user on 01 Septem ber 2020 models (Fan & Zhang, 2000; Fan et al., 2003), wavelet-based functional models (Morris et al., 2003), functional additive models (Müller & Yao, 2008) and quadratic models (Yao & Müller, 2010).”