Spatially Adaptive Bayesian Penalized Splines With Heteroscedastic Errors
By Ciprian M. Crainiceanu, David Ruppert, Raymond J. Carroll, Adarsh Joshi, Billy Goodner. 2007
113 citations recorded by the source corpus.
Subfields: Applied Mathematics, Computer Vision and Pattern Recognition, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/8348e38b0bcf3bc75a3d45a0b515524f72e3938d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Applied Mathematics
- María Durbán
- Paul H. C. Eilers
- Gerda Claeskens
- Brian D. Marx
- Tatyana Krivobokova
- Thomas Kneib
- Matt P. Wand
- Jean D. Opsomer
In Computer Vision and Pattern Recognition
In Statistics and Probability
- María Durbán
- Paul H. C. Eilers
- Thomas Kneib
- Brian D. Marx
- F. J. Breidt
- Tatyana Krivobokova
- Gerda Claeskens
- Matt P. Wand
Citations in the corpus
- Twenty years of P-splines (2015, methodology): “Crainiceanu et al. (2007) did this in one dimension, using truncated power functions.”
- Variational Inference for Heteroscedastic Semiparametric Regression (2015, methodology): “Several approaches to fitting (1) now exist (e.g. Ruppert et al. 2003; Rigby & Stasinopoulos 2005; Crainiceanu et al. 2007).”
- Laplace Variational Approximation for Semiparametric Regression in the Presence of Heteroskedastic Errors (2016, methodology): “As in Crainiceanu et al. (2007), the model structure and conjugate nature of the priors described here lend themselves to a hybrid Gibbs sampling computational approach to fitting our model.”
- Simultaneous Confidence Bands for Penalized Spline Estimators (2010, methodology): “Crainiceanu et al. (2007) considered simultaneous confidence bands when posterior normality for the parameter vector can be assumed.”