Methods of canonical analysis for functional data

By G. He, H. Muller, Jane-ling Wang. 2004

58 citations recorded by the source corpus.

Subfields: Demography, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/20e1fb7607e68ced92fdea5854bde4f0a197332d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Demography

1. H. Muller
2. H. ller
3. T. Hsing
4. Randall Eubank
5. Joel A. Dubin
6. Jane-ling Wang

In Statistics and Probability

1. H. Muller
2. H. ller
3. Jane-ling Wang
4. T. Hsing
5. Joel A. Dubin
6. Wenjing Yang
7. G. He
8. Tomasz Górecki

Citations in the corpus

• Optimal-order bounds on the rate of convergence to normality in the multivariate delta method (2009, background): “[27, 17] for discussion and results on the use of canonical correlations in functional data.”
• Dynamical Correlation for Multivariate Longitudinal Data (2005, methodology): “This latter method is discussed and evaluated under the rubric “smoothing and subsequent subsampling” (method 1, FCA–LP) of He et al. (2004), where various implementations of functional canonical correlation are compared.”
• Functional linear regression via canonical analysis (2010, methodology): “To obtain functional canonical correlations and the corresponding weight functions as needed for FCR, we adopt one of the methods proposed in He et al. (2004).”
• Functional Modelling and Classification of Longitudinal Data * (2005, methodology): “This involves the inversion of a compact operator (He et al., 2003) and therefore requires sensible regularization, as proposed in Leurgans et al. (1993) and further investigated in He et al. (2004).”
• Canonical correlation for stochastic processes (2008, methodology): “He, Müller and Wang [13] (denoted by HMW herafter) developed a notion of canonical correlation for processes of the form (34) through a direct extension of finite-dimensional matrix formulations with integral operators being used in the place of matrices.”
• Selected statistical methods of data analysis for multivariate functional data (2018, methodology): “The idea to project processes on the finite k basis functions has been discussed in He et al. (2004).”
• Functional Modelling and Classification of Longitudinal Data. (2005, methodology): “Principal component analysis (PCA) has become a major tool of multivariate analysis since its introduction by Hotelling (1933); compare Joliffe (2002). This method can be extended directly from MDA to FDA.”