Functional Modelling and Classification of Longitudinal Data.

By H. ller. 2005

147 citations recorded by the source corpus.

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Citations in the corpus

Learning Theory for Distribution Regression (2014, methodology): “Functional data analysis techniques: Finally, the distribution regression task might also be interpreted as a functional data analysis problem (Ramsay and Silverman, 2002, 2005; Müller, 2005), by considering the probability measures xi as functions.”
MULTILEVEL FUNCTIONAL PRINCIPAL COMPONENT ANALYSIS (2009, methodology): “This problem can be solved by trimming eigenvalue-eigenvector pairs where the eigenvalue is negative [Hall, Müller and Yao (2008); Müller (2005), Yao, Müller and Wang (2005)].”
Functional quadratic regression (2010, background): “It follows from results in Müller ( 2005 ) that , as designs become dense , ˆ ξ * I k ( 17 ) andˆξ * P k ( 28 ) can be considered asymptotically equivalent .”
Horvitz--Thompson estimators for functional data: asymptotic confidence bands and optimal allocation for stratified sampling (2009, background): “…with such sampling strategies in finite population has not yet been studied in the functional data analysis literature (Ramsay & Silverman, 2005, Müller, 2005) except by Cardot et al. (2010), where the objective was to reduce the dimension of the data through functional principal components in…”
Penalized Functional Regression (2011, unknown): “Unbiased estimators of cik are easy to obtain as the Riemann sum approximation to the integral ∫ 1 0 Wi(t)ψj (t) dt ; for example, ĉik = ∑Ji j=1 Wi(tij )ψk(tij ) was proposed by Müller and Stadtmüller (2005). This method works”
Functional data analysis for volatility (2011, methodology): “One application is discriminant and cluster analysis to determine or characterize particular time-dynamic patterns (James and Sugar 2003, Müller 2005).”
Functional Additive Models (2008, result): “We note that it follows from results of Müller (2005) that as designs become dense, these best linear (PACE) estimates ξ̂ik, ζ̂im (A.4) converge to those obtained by the more traditional integrationbased estimates, ξ̂ Iik = ni∑ j=2 (Uij − μ̂X(tij ))φ̂k(sij )(sij − si,j−1), (A.5) ζ̂ Iim = ni∑ j=2…”
Conditional quantile analysis when covariates are functions, with application to growth data (2012, methodology): “Such models have been widely studied in the literature (James, 2002; Cardot and Sarda, 2005; Müller and Stadtmüller, 2005; Leng and Müller, 2006; Escabias et al., 2007).”