Multivariate calibration with temperature interaction using two-dimensional penalized signal regression

By Paul H. C. Eilers, Brian D. Marx. 2003

160 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Industrial and Manufacturing Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/bebb89fb617f85b619c346c0e1928ee389d28011

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Simon N. Wood
2. Pedro Delicado
3. Kehui Chen
4. María Durbán
5. Paul H. C. Eilers
6. David Ruppert
7. Yingxing Li
8. H. Muller

In Industrial and Manufacturing Engineering

1. Simon N. Wood
2. María Durbán
3. Paul H. C. Eilers
4. David Ruppert
5. H. Muller
6. Pedro Delicado
7. Luo Xiao
8. Iain D. Currie

In Statistics and Probability

1. María Durbán
2. Paul H. C. Eilers
3. Brian D. Marx
4. Simon N. Wood
5. Yingxing Li
6. David Ruppert
7. Kehui Chen
8. Luo Xiao

Citations in the corpus

• Fast bivariate P‐splines: the sandwich smoother (2010, methodology): “Let P E-M denote the penalty matrix for the E-M estimator, then The first and second penalty terms in bivariate P-splines penalize the columns and rows of Θ , respectively, and are thus called column and row penalties.”
• Correcting for spatial heterogeneity in plant breeding experiments with P-splines (2018, methodology): “To prevent over-fitting, difference penalties down the columns and along the rows are added, and estimation is based on penalised-likelihood methods (Eilers and Marx, 2003).”
• Twenty years of P-splines (2015, background): “For example, Eilers and Marx (2003) and Marx and Eilers (2005) extended PSR to allow interaction with a discrete variable and to the twodimensional case where each x is not a vector but a matrix.”
• Low‐Rank Scale‐Invariant Tensor Product Smooths for Generalized Additive Mixed Models (2006, methodology): “Either low rank approximations to thin plate splines have been employed (Kammann and Wand, 2003; Ruppert, Wand and Carroll, 2003) or tensor product P-splines have been suggested, with the single penalty given by the Kronecker product of the penalties associated with the marginal bases from which the”
• Smoothing and forecasting mortality rates (2004, methodology): “Theoretical aspects of P-splines are discussed in Wand (1999), Ruppert and Carroll (2000), Aerts et al.”
• Modelling function‐valued stochastic processes, with applications to fertility dynamics (2017, methodology): “In related applications such as mortality analysis, the decomposition into age and year has been studied by Eilers and Marx (2003), Currie et al. (2004, 2006) and Eilers et al. (2006), by using P-splines.”