Low-rank scale-invariant tensor product smooths for generalized additive mixed models.

@article{Wood2006LowrankST,
  title={Low-rank scale-invariant tensor product smooths for generalized additive mixed models.},
  author={Simon N. Wood},
  journal={Biometrics},
  year={2006},
  volume={62 4},
  pages={
          1025-36
        }
}
A general method for constructing low-rank tensor product smooths for use as components of generalized additive models or generalized additive mixed models is presented. A penalized regression approach is adopted in which tensor product smooths of several variables are constructed from smooths of each variable separately, these "marginal" smooths being represented using a low-rank basis with an associated quadratic wiggliness penalty. The smooths offer several advantages: (i) they have one… CONTINUE READING
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References

Publications referenced by this paper.
SHOWING 1-10 OF 14 REFERENCES

Some aspects of the spline smoothing approach to nonparametric regression curve fitting

  • B. W. Cambridge Silverman
  • 1985
Highly Influential
4 Excerpts

A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria

  • D. Ruppert, M. P. Wand, R. J. Carroll
  • ISBN 3-900051-00-3,
  • 2003

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