Mon premier blog

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

Feb 28, 2014 - Generalized additive models were estimated to explore linear and nonlinear relations among income inequality, poverty, and child maltreatment. And I will illustrate the results with nice plots along the way. May 2, 2012 - I've been fitting some time series data using a Poisson general additive model using SAS's PROC GAM . Mar 28, 2012 - Generalized linear models (GLMs) may be extended by programming one. Mar 2, 2014 - Below, I will proceed from a simple linear regression to a generalized additive model to an ordered logistic regression analysis. Mar 7, 2014 - Our modelling framework is built directly on the lactation curve model. April 2014 Application of generalized additive models to examine ontogenetic and seasonal distributions of spiny dogfish (Squalus. Aug 28, 2012 - Controlling for seasonal and meteorological conditions was done using three different models— a generalized linear model, a generalized additive model, and a generalized additive model on previously filtered data. Jul 13, 2012 - The methods available in Generalized Additive Models are implementations of techniques developed and popularized by Hastie and Tibshirani (1990). Jan 19, 2012 - In the ALV model structure the nonlinear components of the regression equations are represented as generalized additive model (GAM), or generalized additive mixed-effects model (GAMM). Apr 10, 2014 - Published online on 10. 18.3 Generalized additive models. 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. Sep 8, 2009 - Analysing spatio-temporal patterns of the global NO2-distribution retrieved from GOME satellite observations using a generalized additive model. Jan 1, 2011 - The relationships have been quantified after controlling for long-term trends, seasonality, weekly emissions, spatial variation, and temporal persistence using the framework of a generalized additive modelling (GAM). Generalized linear models (GLMs) extend linear regression to models with a non-Gaussian, or even discrete, response. We extend this traditional model onto a well-known statistical modelling framework: the generalised additive model (GAM; Hastie and Tibshirani 1990).