Fast function-on-scalar regression with penalized basis expansions.

  title={Fast function-on-scalar regression with penalized basis expansions.},
  author={Philip T. Reiss and Lei Huang and Maarten Mennes},
  journal={The international journal of biostatistics},
  volume={6 1},
  pages={Article 28}
Regression models for functional responses and scalar predictors are often fitted by means of basis functions, with quadratic roughness penalties applied to avoid overfitting. The fitting approach described by Ramsay and Silverman in the 1990 s amounts to a penalized ordinary least squares (P-OLS) estimator of the coefficient functions. We recast this estimator as a generalized ridge regression estimator, and present a penalized generalized least squares (P-GLS) alternative. We describe… CONTINUE READING

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