Smoothing parameter selection for a class of semiparametric linear models

By Philip T. Reiss, R. Todd Ogden. 2009

210 citations recorded by the source corpus.

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Generalized additive models for large data sets (2015, methodology): “The difficulty then arises that GCV and related criteria are known to produce a small proportion of substantial overfits, with increasing problems as the sample size decreases (see for example Reiss and Ogden (2009)).”
Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models (2011, methodology): “To illustrate this, this section revisits an example from Reiss and Ogden (2009) but makes use of the new method to employ a more general model than theirs, based on non-Gaussian errors with multiple penalties.”
Tests for Error Correlation in the Functional Linear Model (2010, background): “…and Fraiman (2002), Malfait and Ramsay (2003), Cardot et al. (2003), Chiou, Müller, and Wang (2004), Müller and Stadtmüller (2005), Yao, Müller, and Wang (2005a, 2005b), Cai and Hall (2006), Chiou and Müller (2007), Li and Hsing (2007), Reiss and Ogden (2007, 2009, 2010), among many others.”
Modelling Palaeoecological Time Series Using Generalised Additive Models (2018, background): “Several recent results have shown that GCV, under certain circumstances, has a tendency to under smooth, resulting in fitted splines that are overly wiggly (Reiss and Ogden, 2009).”