On the use of generalized additive models in time-series studies of air pollution and health.

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Health Effects of Air Pollution: A Statistical Review (2003, methodology): “However, recently the GAM software implementation in S-Plus has been called into question and more systematic comparisons between GAM and GLM are underway (Dominici et al., 2002b; Ramsayet al., 2003).”
Unifying Optimization Algorithms to Aid Software System Users: optimx for R (2011, methodology): “Sometimes, these inaccuracies can have serious implications as exemplified by Dominici (2002). Three of the methods in optim ("Nelder-Mead", "CG", "BFGS") were chosen and adapted by one of us (Nash 1979) for use on computers with at most 8K bytes (that is K, not M) of memory for both program and pro”
Comparing the Performance of Statistical Models for Predicting PM10 Concentrations (2014, methodology): “The method allows for non-parametric adjustments of the non-linear confounding effects of variables (Dominici et al., 2002).”
Time-dependent spectral analysis of epidemiological time-series with wavelets (2007, methodology): “Spectral techniques have also been used to remove seasonal components before using regression models for analysing the relation between levels of particulate air pollution and daily mortality (Schwartz 1993; Dominici et al. 2002).”
Parsimonious additive models (2007, background): “These models are thus applied in several domains including economics [35,4], engineering [41] and public health [2,14].”