refund: Regression with Functional Data

By Ciprian M. Crainiceanu, Philip T. Reiss, J. Goldsmith, Lei Huang, Lan Huo, Fabian Scheipl. 2013

242 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/925f559062b4978b217755fbe779e44e1f557fb3

How researchers see this paper

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

1. Sarah Brockhaus
2. Gina‐Maria Pomann
3. Torsten Hothorn
4. Sujit Kumar Ghosh
5. Ana-Maria Staicu
6. Clara Happ
7. Peter Buhlmann
8. Sonja Greven

Citations in the corpus

• The functional linear array model (2015, methodology): “We can then use penalized function-on-function regression (PFFR) proposed by Ivanescu et al. (2014) and Scheipl et al. (2014) and implemented in the R add-on package refund (Crainiceanu et al., 2014) as a benchmark.”
• Multivariate Functional Principal Component Analysis for Data Observed on Different (Dimensional) Domains (2015, methodology): “The MFPCA is based onM1 = M2 = 8 univariate functional principal components that are calculated by the PACE algorithm (Yao, Müller, and Wang 2005) with penalized splines to smooth the covariance function, as implemented in the R package refund (Crainiceanu et al. 2014).”
• A Two Sample Distribution-Free Test for Functional Data with Application to a Diffusion Tensor Imaging Study of Multiple Sclerosis (2016, methodology): “In more recent versions of this study (Crainiceanu et al. (2012), Staicu et al. (2012); Gertheiss et al. (2013a); McLean et al. (2014), Ivanescu et al.”