Generalized Additive Models: Some Applications

By Trevor J. Hastie, Robert Tibshirani. 1987

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

InterpretML: A Unified Framework for Machine Learning Interpretability (2019, background): “EBM has a few major improvements over traditional GAMs (Hastie and Tibshirani, 1987).”
Forecasting at Scale (2018, neutral): “This speciﬁcation is similar to a generalized additive model (GAM) [Hastie and Tibshirani, 1987], a class of regression models with potentially non-linear smoothers applied to the regressors.”
Learning Sparse Nonparametric DAGs (2019, methodology): “Additive models In an additive model (Hastie and Tibshirani, 1987; Ravikumar et al., 2009), we assume that fj(X) = P k 6=j fjk(Xk) for some fjk 2 H1(R).”
Model robust regression: combining parametric, nonparametric, and semiparametric methods (2001, methodology): “2) for the nonparametric and modelrobust procedures is supported by Silverman (1985) and Hastie and Tibshirani (1987), who use contidence intervals of this form when discussing spline regression and general additive models, respectively.”
Explainable AI: A Review of Machine Learning Interpretability Methods (2020, background): “GA2Ms are generalized additive models (GAM) [67], but with a few tweaks that set them apart, in terms of predictive power, from traditional GAMs.”
Nonparametric binary regression using a Gaussian process prior (2007, methodology): “Hastie and Tibshirani [10] proposed a local scoring method for additive models.”
Convolutional neural networks for vibrational spectroscopic data analysis. (2017, methodology): “We also considered Logistic Regression, which can be seen as a very simple neural network with no hidden layers, where the input passes directly to the output layer through an activation function: 4ðtÞ 1⁄4 1 1þe t [27].”
New approaches to regression by generalized additive models and continuous optimization for modern applications in finance, science and technology (2007, methodology): “These models are fitted through local scoring algorithm using a scatterplot smoother as building blocks proposed by Hastie and Tibshirani (1987).”
Modeling Ordered Choices: A Primer (2010, unknown): “A counterpart to the Hausman [1978] test for the linear model seems desirable, however, unlike the linear case, the fixed effects estimator for this nonlinear model is inconsistent even when it is the appropriate estimator (due to the incidental parameters problem).”
Effects of sample and grid size on the accuracy and stability of regression‐based snow interpolation methods (2009, unknown): “Generalized additive models (GAMs) are nonparametric extensions of generalized linear models (GLMs) that estimate response curves with a nonparametric smoothing function rather than with parametric terms (Hastie and Tibshirani, 1987).”
Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors (2014, methodology): “To fix ideas, we will discuss a dataset from Hastie and Tibshirani (1987) about a retrospective sample of males in a heart-disease high-risk region of the Western Cape, South Africa.”
Accounting for spatial pattern when modeling organism- environment interactions (2002, unknown): “Additive regression models (Hastie and Tibshirani 1987) have also be used to model complex, non-parametric relationships between abundance and environmental predictors (Yee and Mitchell 1991).”