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Generalized Additive Models: An Introduction with R (Chapman & Hall/CRC Texts in Statistical Science) 1st Edition
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- The PQL algorithm used for fitting GAMM has been brought into question before, especially for binary data where the resulting variance component parameter estimates are highly biased (see for example Breslow's Whither PQL?) to the point that many do not recommend using PQL for binary data (you can use a Bayesian model instead in this case). The book makes no mention of this and only focuses on the diagnostics of binary data. I believe this issue should be brought up with at least a brief section on optional methods of fitting the GAMM.
- Technically GAM models can use any type of basis function, not just splines, so the title of the book is a bit misleading
- (November 2012, update) I found myself using the cairo temperature example in a time series course, when discussing nonparametric based methods (including mixed models) as alternatives to more traditional ARIMA models. To my surprise, I found strong autocorrelation still present in the final model proposed in the book for the temperature data. Although the example is perhaps intended strictly for academic purposes, this finding was quite disappointing.