Estimating Optimal Transformations for Multiple Regression and Correlation.

By Leo Breiman, Jerome H. Friedman. 1985

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Citations in the corpus

Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation (2013, methodology): “We sample from F by using backﬁtting (Breiman and Friedman, 1985) to generate g and h (cid:63) , estimates of g and h , and then bootstrapping the residuals.”
Additive models with trend filtering (2017, methodology): “Two algorithms closely related to (backfitting for) additive models are the alternating least squares and alternating conditional expectations methods, from van der Burg and de Leeuw [1983] and Breiman and Friedman [1985], respectively. The work of Buja et al. [1989] advocates for the use of additiv”
Bayesian Learning of Kernel Embeddings (2016, methodology): “Figure 4: Graphical model representing an equivalence class of DAGs for the Ozone dataset from Breiman and Friedman (1985), learned using the PC algorithm following the approach in Flaxman et al.”
Kernel dimension reduction in regression (2009, methodology): “eling assumptions are imposed on the conditional distribution P(Y|X) or the regression E(Y|X). These methods include ordinary least squares, partial least squares, canonical correlation analysis, ACE [4], projection pursuit regression [12], neural networks and LASSO [29]. These methods can be eﬀecti”
CAM: Causal Additive Models, high-dimensional order search and penalized regression (2013, neutral): “The question of closedness of additive models has also been studied in [1], for example; see also [29].”
Mixtures of g Priors for Bayesian Variable Selection (2008, methodology): “Our last example uses the ground-level ozone data analyzed by Breiman and Friedman (1985) and, more recently, by Miller (2001) and Casella and Moreno (2006).”
Gradient-based kernel method for feature extraction and variable selection (2012, methodology): “In a previous study [1], the four variables RM, TAX, PTRATIO and LSTAT are considered to have major contribution.”
Additive Regression and Other Nonparametric Models (1985, background): “A less flexible, but more easily interpretable, generalization of the additive model is Model 8 f(xl,... xj) = g(EJ f (xj)) which was considered by Winsberg and Ramsay (1980) (see also de Leeuw, et al., 1976; Young, et al., 1976; and Breiman and Friedman, 1985).”
Kernel Estimation of Partial Means and a General Variance Estimator (1994, methodology): “Unlike the alternating conditional expectation estimator for additive models (ACE, see [2]), the partial mean is an explicit functional, so the kernel estimator will not require iteration.”
Internet banking service quality, e-customer satisfaction and loyalty: the modified e-SERVQUAL model (2020, background): “R(2) is significant for the evaluation of a structural model (Breiman and Friedman, 1985).”
An Information-Theoretic Approach to Transferability in Task Transfer Learning (2019, methodology): “(6) can be solved efﬁciently using the Alternating Conditional Expectation (ACE) algorithm [14] for discrete X , or using the neural network approach based on Generalized Maximal HGR Correlation [15] for a generic X .”
Regression Modeling Strategies (2003, methodology): “[55] J. H. Friedman.”