Wacky regression

September 23, 2015 — May 2, 2019

classification

functional analysis

model selection

nonparametric

optimization

regression

I used to maintain a list of regression methods that were almost nonparametric, but as fun as that category was, I was not actually using it, so I broke it apart into more conventional categories.

See bagging and boosting methods, neural networks, functional data analysis, Gaussian process regression and randomised regression.

1 References

Fomel. 2000. “Inverse B-Spline Interpolation.”

Friedman, Jerome H. 2001. “Greedy Function Approximation: A Gradient Boosting Machine.” The Annals of Statistics.

———. 2002. “Stochastic Gradient Boosting.” Computational Statistics & Data Analysis, Nonlinear Methods and Data Mining,.

Friedman, Jerome, Hastie, and Tibshirani. 2000. “Additive Logistic Regression: A Statistical View of Boosting (With Discussion and a Rejoinder by the Authors).” The Annals of Statistics.

Johnson, and Zhang. 2014. “Learning Nonlinear Functions Using Regularized Greedy Forest.” IEEE Transactions on Pattern Analysis and Machine Intelligence.

Jones. 1992. “A Simple Lemma on Greedy Approximation in Hilbert Space and Convergence Rates for Projection Pursuit Regression and Neural Network Training.” The Annals of Statistics.

Scornet. 2014. “On the Asymptotics of Random Forests.” arXiv:1409.2090 [Math, Stat].

Scornet, Biau, and Vert. 2014. “Consistency of Random Forests.” arXiv:1405.2881 [Math, Stat].

Tropp. 2004. “Greed Is Good: Algorithmic Results for Sparse Approximation.” IEEE Transactions on Information Theory.

Vanli, and Kozat. 2014. “A Comprehensive Approach to Universal Piecewise Nonlinear Regression Based on Trees.” IEEE Transactions on Signal Processing.