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A new methodology for linear regression with both curve response and curve regressors, which is described in Cho, Goude, Brossat and Yao (2013) <doi:10.1080/01621459.2012.722900> and (2015) <doi:10.1007/978-3-319-18732-7_3>. The key idea behind this methodology is dimension reduction based on a singular value decomposition in a Hilbert space, which reduces the curve regression problem to several scalar linear regression problems.
Package details |
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Author | Amandine Pierrot with contributions and/or help from Qiwei Yao, Haeran Cho, Yannig Goude and Tony Aldon. |
Maintainer | Amandine Pierrot <amandine.m.pierrot@gmail.com> |
License | LGPL (>= 2.0) |
Version | 0.1.2 |
Package repository | View on CRAN |
Installation |
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