A VARIABLE SPAN SMOOTHER

By Jerome H. Friedman. 1984

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Citations in the corpus

Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation (2013, methodology): “This method is also applicable to stochastic gradient boosting (SGB) (Friedman 2002). Plate et al. (2000) plotted NN predictions in a scatterplot for each variable by sampling points from covariate space. Among the existing literature, this work is the most similar to ICE, but was only applied to NN”
Critical note on the application of the “two-third” spline (2021, background): “Span-related detrending options such as the Friedman variable span smoother (Friedman, 1984) and locally estimated scatterplot smoothing (LOESS) are probably also affected by this effect.”
Applied Nonparametric Regression: The kernel method (1990, unknown): “pursuit regression (Friedman and Stuetzle 1981) or ACE (Breiman and Friedman 1985) it is vital to have a good choice of smoothing parameter for one-dimensional smoothers that are elementary building blocks for these procedures. In the following sections various methods for choosing the smoothing par”
Regression Modeling Strategies (2003, unknown): “Uses nonparametric “super smoother” [72] 2.”
Visualization and exploration of high-dimensional functions using the functional anova decomposition (1996, methodology): “Whereas PPR ts a few highly exible functions,FNN's t a large numberM of these simple sigmoidal functions.”
KEPLER FLARES. II. THE TEMPORAL MORPHOLOGY OF WHITE-LIGHT FLARES ON GJ 1243 (2014, methodology): “Davenport et al. 2015, in preparation). The degree of self-similarity between the classical flares in our template sample is remarkable. The “impulsive phase” (rise and decay) of the flare dominates the characteristic timescale (t1/2) used in generating the template. The slow decay phase, which cont”
An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression (1992, methodology): “Instead of a weighted mean, a linear regression estimate is computed in every neighborhood (Cleveland 1979; Cleveland and Devlin 1988; Friedman 1984).”
Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis (2001, unknown): “Uses nonparametric “super smoother” [62] 2.”
Time series forecasting with neural networks: a comparative study using the air line data (2008, unknown): “It is rather more difﬁcult in practice to make predictions using a PPR model since the ‘activation function’ is estimated using the ‘supersmoother’ method (Friedman, 1984) which does not lend itself readily to extrapolation.”
Applied Nonparametric Regression (1994, unknown): “pursuit regression (Friedman and Stuetzle 1981) or ACE (Breiman and Friedman 1985) it is vital to have a good choice of smoothing parameter for one-dimensional smoothers that are elementary building blocks for these procedures. In the following sections various methods for choosing the smoothing par”
Predictive 5-year survivorship model of cystic fibrosis. (2001, methodology): “The smoothed-curve fit of our model (44, 45) closely approximated this ideal.”
A Tree-Ring-Based Reconstruction of Delaware River Basin Streamflow Using Hierarchical Bayesian Regression (2013, methodology): “The ‘‘Friedman Super Smoother’’ was the primary option used to reduce the inﬂuence of disturbance in each series (Friedman 1984; Buckley et al. 2010).”