Penalized spline models for functional principal component analysis
By Fang Yao, Thomas C. M. Lee. 2006
165 citations recorded by the source corpus.
Subfields: Genetics, Molecular Biology, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/a1e71c35854436a5cd8a3124ba8f6bbd4b3c7611
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Citations in the corpus
- Robust functional principal components: A projection-pursuit approach (2011, background): “More recent work on estimation of the principal components and the covariance function includes Gervini (2006), Hall and Hosseini-Nasab (2006), Hall, Müller and Wang (2006) and Yao and Lee (2006).”
- 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 Data Analysis (2016, methodology): “For the much more difficult but commonly encountered situation of sparse functional data, the FPCA approach was investigated in Shi, Weiss & Taylor (1996); Staniswalis & Lee (1998); James, Hastie & Sugar (2000); Rice & Wu (2001); Yao, M¨uller & Wang (2005a); Yao & Lee (2006); and Paul & Peng (2009).”
- Functional Additive Models (2008, background): “…the estimated population components such as mean function, eigenfunction, and eigenvalue estimates in model (A.2) or (A.3) (see Yao et al. 2005a; Hall and HosseiniNasab 2006), a key step in the mathematical analysis is to establish exact upper bounds of |ξ̂ik − ξik| and |ζ̂im − ζim|, that are…”
- Modelling function‐valued stochastic processes, with applications to fertility dynamics (2017, background): “Uniform convergence of the empirical working processes {⇠̂i,j(tim), 1 i n, 1 m Mi} to the target processes {⇠i,j(t), t 2 T } then guarantees the consistency of the estimates of the eigenfunctions jk and the eigenvalues ⌘jk (Yao and Lee 2006).”
- Uniform convergence rates for nonparametric regression and principal component analysis in functional/longitudinal data (2010, background): “A partial list of such work includes Ramsay and Silverman (2005), Lin and Carroll (2000), Wang (2003), Yao, Müller and Wang (2005a, 2005b), Yao and Lee (2006) and Hall, Müller and Wang (2006).”
- A survey of functional principal component analysis (2014, unknown): “1996 , Yao & Lee 2006 ) .”
- Fast bivariate P‐splines: the sandwich smoother (2010, methodology): “Local polynomial smoothing is a popular method in estimating covariance functions (see e.g., Yao et al. (2005) or Yao and Lee (2006)) while other smoothing methods such as kernel (Staniswalis and Lee, 1998) and penalized splines (Di et al. , 2009) have also been used.”