General additive modeling

Several empirical approaches for NMR spectra prediction are based on the availability of large NMR spectral databases. By using special methods for encoding substructures that correspond to particular parts of the NMR spectrum, the correlation of substructures and partial spectra can be modeled. Substructures can be encoded by using the additive model greatly developed by Pretsch [11] and Clerc [12]. The authors represented skeleton structures and substituents by individual codes and calculation rules. A more general additive model was introduced... 

Hastie, T. J., and Tibshirani, R. J., Generalized Additive Models. Chapman and Hall, New York, 1990. 

In-Kwon Yeo received the PhD degree in Statistics from University of Wisconsin-Madison in 1997. He joined the Department of Control and Instrumentation Engineering, Kangwon National University as a visiting professor in 2000 and the Division of Mathematics and Statistical Informatics, Chonbuk National University as an assistant professor in Korea. He is currently an associate professor at the Department of Statistics, Sookmyung Women s University. His current research interests include data transformations, multivariate time series analysis and generalized additive models. 

Chilcoat, H.D. and Schutz, C.G., Age-specific patterns of hallucinogen use in the U.S. population an analysis using generalized additive models, Drug Alcohol Depend., 43, 143, 1996. 

Verify that S-Plus 5.1 performs a General Additive Model with Gaussian error Distribution and identity link problem correctly to 3 significant digits... 

A third approach proposed by Mandema et al. (16) was an improvement on the Maitre et al. (15) approach. The first step is similar to that proposed by Maitre et al. (15), but in the second step individual PK/PD parameters are regressed against covariates using generalized additive modeling (GAM). In the final step, NONMEM is used to optimize and finalize the population model. The approach does not discuss how a reduction in the dimensionahty of the covariate vector should be handled. 

Exploratory modeling using modem statistical modeling techniques such as generalized additive modeling (GAM) (15), cluster analysis, and tree-based modehng (TBM) to reveal structure in the data and initially select explanatory covariates. 

T. Hastie and R. Tibshirani, Generalized Additive Models. Chapman Hall, London, 1990. 

Generalized additive modeling (GAM) was applied to each of the output data sets. A selection criteria of a = 0.05 and a frequency cutoff of 0.50 was applied for continued investigation of a covariate that is, GAM had to select a covariate for inclusion in 50% or more of the models from the 100 bootstrap fits for the covariate to be considered for further investigation. 

The next covariate screening approach would be to use a regression-based method and take a more rigorous statistical approach to the problem. Using the generalized additive model (GAM) procedure in SAS, a LOESS smooth was applied to the continuous covariates wherein the procedure was allowed to identify the optimal smoothing parameter for each covariate tested. Two dependent variables were examined t, and the EBE for CL. To avoid possible skewness in the residuals,... 

Values represent the p-value from the analysis. Legend GAM, generalized additive model BSA, body surface area IBW, ideal body weight BMI, body mass index and LBW, lean body weight. 

Covariate selection Examine covariate addition to model using generalized additive modeling (GAM) or equivalent stepwise insertion techniques. 

---