clr-package: Curve Linear Regression
In clr: Curve Linear Regression via Dimension Reduction

Description Details Author(s) References

clr provides functions for curve linear regression via dimension reduction.

The package implements a new methodology for linear regression with both curve response and curve regressors, which is described in Cho et al. (2013) and Cho et al. (2015). The CLR model performs a data-driven dimension reduction, based on a singular value decomposition in a Hilbert Space, as well as a data transformation so that the relationship between the transformed data is linear and can be captured by simple regression models.

Amandine Pierrot <amandine.m.pierrot@gmail.com>

with contributions and help from Qiwei Yao, Haeran Cho, Yannig Goude and Tony Aldon.

These provide details for the underlying clr methods.

Cho, H., Y. Goude, X. Brossat, and Q. Yao (2013) Modelling and Forecasting Daily Electricity Load Curves: A Hybrid Approach. Journal of the American Statistical Association 108: 7-21.

Cho, H., Y. Goude, X. Brossat, and Q. Yao (2015) Modelling and Forecasting Daily Electricity Load via Curve Linear Regression. In Modeling and Stochastic Learning for Forecasting in High Dimension, edited by Anestis Antoniadis and Xavier Brossat, 35-54, Springer.

clr documentation built on July 29, 2019, 9:03 a.m.