Flexible smoothing with P-splines: a unified approach

By Iain D. Currie, María Durbán. 2002

162 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/2bc394580a21149d98578b22dd3da3c2ec75e3ec

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In Statistics and Probability

Citations in the corpus

Smoothing and forecasting mortality rates (2004, methodology): “Articles which emphasize applications include Marx and Eilers (1999), Parise et al. (2001), Coull et al. (2001a, 2001b) and Currie and Durban (2002).”
Correcting for spatial heterogeneity in plant breeding experiments with P-splines (2018, methodology): “As will be seen, the mixed model representation of P-splines (Currie and Durban, 2002; Wand, 2003) provides us”
Generalized structured additive regression based on Bayesian P-splines (2006, background): “Lin and Zhang (1999), Currie and Durban (2002), Wand (2003) and the book by Ruppert, Wand and Carroll (2003). Indeed, the approach is very promising and has several distinct advantages, e.”
P-spline ANOVA-type interaction models for spatio-temporal smoothing (2011, methodology): “There are several alternatives depending on the bases and the penalty used, for example, Wand (2003) described the representation with truncated power functions as bases, and Currie and Durbán (2002) described the representation using B-splines bases.”
A Note on Penalized Spline Smoothing With Correlated Errors (2007, methodology): “For penalized spline fitting, Currie and Durban (2002) and Durban and Currie (2003) presented a strategy for smoothing with correlated errors and selecting the correlation structure based on the likelihood.”
Penalized Wavelets: Embedding Wavelets into Semiparametric Regression (2011, methodology): “The model considered by Durbán et al. [18] takes the basic form yij = f(xij) + gi(xij) + εij , εij ind. ∼ N(0, σ2ε) where, for 1 ≤ i ≤ m and 1 ≤ j ≤ ni, (xij , yij) denotes the jth predictor/response pair for the ith subject.”
Twenty years of P-splines (2015, methodology): “Currie and Durbán (2002) used this approach and extended it to handle heteroscedastic or autocorrelated noise.”