On rates of convergence in functional linear regression

By Yehua Li, T. Hsing. 2007

124 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/8f2220b7edf2407b6af577a97a0efc44846c1838

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Citations in the corpus

Truncated linear models for functional data (2014, background): “…Apanasovich and Goldstein (2008) addressed mean-squared prediction error in functional linear regression; and Cardot et al. (2007), Hall and Horowitz (2007), Li and Hsing (2007), Cai and Yuan (2012) and Johannes and Schenk (2013) discussed convergence rates of estimators of a and b in model (1.1).”
A Reproducing Kernel Hilbert Space Approach to Functional Linear Regression (2010, methodology): “…years for the functional linear regression [James (2002); Cardot, Ferraty and Sarda (2003); Ramsay and Silverman (2005); Yao, Müller and Wang (2005); Ferraty and Vieu (2006); Cai and Hall (2006); Li and Hsing (2007); Hall and Horowitz (2007); Crambes, Kneip and Sarda (2009); Johannes (2009)].”
Locally Sparse Estimator for Functional Linear Regression Models (2017, unknown): “Smooth estimators of β(t ) based on a direct roughness penalty onβ(t )were studied inCardot, Ferraty, and Sarda (2003), Li and Hsing (2007), and Crambes, Kneip, and Sarda (2009).”
Adaptive functional linear regression (2011, background): “j gj: ( 14 ) Lemma 4 in Section 4 shows that the variance Var( ~ dj) =”
Smoothing splines estimators for functional linear regression (2009, methodology): “For the second method, regularization is obtained through a penalized least squares approach after expanding α in some basis (such as splines): see Ramsay and Dalzell [24], Eilers and Marx [12], Cardot, Ferraty and Sarda [7] or Li and Hsing [21].”
Functional Principal Component Regression and Functional Partial Least‐squares Regression: An Overview and a Comparative Study (2017, methodology): “For instance, methods based on the use of basis functions can be found in Hastie and Mallows (1993), Marx and Eilers (1996), Ramsay and Silverman (2006), Li and Hsing (2007), and Crambes, Kneip and Sarda (2009), among others.”