A COMPARISON OF MIXED MODEL SPLINES FOR CURVE FITTING
By Sue J. Welham, Brian R. Cullis, Michael G. Kenward, Robin Thompson. 2007
90 citations recorded by the source corpus.
Subfields: Computational Mechanics, Control and Systems Engineering, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/1db0c35bac9123a746f498d7cfd5b4cf6759fa62
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
- Matt P. Wand
- John Thomas Ormerod
- S. Spiriti
- D. Young
- Randall Eubank
- María Durbán
- Dae-Jin Lee
- Philip Smith
In Control and Systems Engineering
In Statistics and Probability
- Matt P. Wand
- John Thomas Ormerod
- María Durbán
- Dae-Jin Lee
- S. Spiriti
- D. Young
- Randall Eubank
- Philip Smith
Citations in the corpus
- P-spline ANOVA-type interaction models for spatio-temporal smoothing (2011, methodology): “…set of models (random effects models, correlated data or longitudinal studies, survival analysis), and the use of the methodology already developed for mixed models for the estimation and inference (see Brumback and Rice, 1998; Verbyla et al., 1999; Wand, 2003; Welham et al., 2007, among others).”
- ON SEMIPARAMETRIC REGRESSION WITH O'SULLIVAN PENALIZED SPLINES (2008, unknown): “Nychka & Cummins, 1996; Cantoni & Hastie, 2002; Welham et al., 2007 ).”
- Penalized Wavelets: Embedding Wavelets into Semiparametric Regression (2011, methodology): “[70] Welham, S.J., Cullis, B.R., Kenward, M.G. and Thompson, R. (2007).”
- Knot selection for least-squares and penalized splines (2013, background): “5] 40 ) that P-splines with fewer knots than observations are ‘easier’ to compute than their smoothing spline relatives.”