Single and multiple index functional regression models with nonparametric link

By Dong Chen, Peter Hall, H. Muller. 2011

202 citations recorded by the source corpus.

Fully nonparametric methods for regression from functional data have poor accuracy from a statistical viewpoint, reflecting the fact that their convergence rates are slower than nonparametric rates for the estimation of high-dimensional functions. This difficulty has led to an emphasis on the so-called functional linear model, which is much more flexible than common linear models in finite dimension, but nevertheless imposes structural constraints on the relationship between predictors and responses. Recent advances have extended the linear approach by using it in conjunction with link functions, and by considering multiple indices, but the flexibility of this technique is still limited. For example, the link may be modeled parametrically or on a grid only, or may be constrained by an assumption such as monotonicity; multiple indices have been modeled by making finite-dimensional assumptions. In this paper we introduce a new technique for estimating the link function nonparametrically, and we suggest an approach to multi-index modeling using adaptively defined linear projections of functional data. We show that our methods enable prediction with polynomial convergence rates. The finite sample performance of our methods is studied in simulations, and is illustrated by an application to a functional regression problem.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f59f3c19a57398abbd7e33300a73b7aa56cb49f5

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Citations in the corpus

Functional additive regression (2015, background): “By Yingying Fan1, Gareth M. James and Peter Radchenko2”
Generalized additive models for functional data (2013, unknown): “(2) This model (see James and Silverman, 2005; Ait-Säıdi et al., 2008; Chen et al., 2011) has an important drawback from the practical point of view: the procedure to select the optimal projection β.”
Functional Data Analysis (2016, background): “…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 2011).”