10 articles
Additive model
The Additive model is a regression technique where the effect of each predictor is the sum of its separate contributions. It is a core structure of Generalized Additive Models (GAMs).
Additive regression
Additive regression is a nonparametric regression method that models a response as a sum of smooth functions of individual predictors, keeping linear-model interpretability while learning curved relationships.
Classification and regression tree
Classification and regression trees (CART) is a decision-tree method that predicts outcomes by recursively splitting data, using the Gini index for classification and squared residuals for regression.
Conditional density estimation
Conditional density estimation (CDE) is the statistical task of estimating the full conditional density p(y|x) from data, supplying modes, prediction intervals, and samples that ordinary regression cannot.
Fréchet regression
Fréchet regression is a statistical method for regressing random objects, such as probability distributions, covariance matrices, or shapes, on Euclidean predictors, introduced by Petersen and Müller in 2019.
Isotonic regression
Isotonic regression is a nonparametric method that fits a nondecreasing function to ordered data by minimizing weighted squared error, used for probability calibration and dose-response estimation.
Kernel regression
Kernel regression is a nonparametric method for estimating a regression function from data without a fixed parametric form, using weighted averages such as the Nadaraya–Watson estimator.
Rank regression
Rank regression is a nonparametric regression method that fits models to the ranks of the data instead of raw values, reducing sensitivity to outliers and distributional assumptions.
Recursive partitioning
Recursive partitioning is a statistical method that builds classification and regression models by repeatedly splitting a dataset into smaller subgroups, producing a binary tree whose leaves carry predictions.
Varying coefficient model
A varying coefficient model is a regression method where coefficients vary smoothly with a covariate such as time, credited to Hastie and Tibshirani's 1993 paper.