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Generalized Additive Models R.J. Tibshirani, T.J. Hastie
Publisher: Chapman and Hall/CRC

April 2014 Application of generalized additive models to examine ontogenetic and seasonal distributions of spiny dogfish (Squalus. Jun 29, 2013 - General Additive Models, Check mark. Sep 8, 2009 - Analysing spatio-temporal patterns of the global NO2-distribution retrieved from GOME satellite observations using a generalized additive model. Mar 8, 2010 - The associations of LV mass, LV end-diastolic volume, mass-to-volume (M/V) ratio, and ejection fraction with BMI, WC, WHR, and FM were displayed graphically using generalized additive models for both sexes. Dec 5, 2010 - Luckily, R has a package called 'gam' (Generalized Additive Models) that allows us to fit a loess regression using the binomial family and a logit link function similar to the glm package. Nov 26, 2006 - GNU R package for estimating vector generalized additive models. Apr 10, 2014 - Published online on 10. Mar 7, 2014 - Our modelling framework is built directly on the lactation curve model. Boosted Tree Classifiers and Regression, Check mark. May 25, 2011 - In a conference paper about consumer risk scoring, Wensui mentioned that generalized additive model (GAM) provides the ability to detect the nonlinear relationship between risk behavior and predictors [Ref. Combining Groups (Optimal Binning), Check mark. Dec 23, 2008 - Generalized Additive Models (Monographs on Statistics and Applied Probability) Chapman & Hall/CRC | ISBN: 0412343908 | 1990-06-01 | PDF | 352 pages | 13 Mb. (MBI) principle that simultaneously generalizes the maximum entropy and standard least squares principles, and leads to a matrix approximation that is optimal among all generalized additive models in a certain natural parameter space. Feb 20, 2014 - The normal distribution and statistical modelling based on normal statistics  Maximum likelihood. Mar 18, 2014 - Application of different models yields different prediction maps. General EM & k-Means Cluster Analysis, Check mark. In general, the generalized additive model (GAM) tends to identify more potential sites while the random forest tends to identify lesser number of potential sites. We extend this traditional model onto a well-known statistical modelling framework: the generalised additive model (GAM; Hastie and Tibshirani 1990).