Original Contribution: Stacked generalization
Neural Networks
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Nonparametric time series prediction: A semi-functional partial linear modeling
Journal of Multivariate Analysis
Additive prediction and boosting for functional data
Computational Statistics & Data Analysis
Dimension reduction in functional regression with applications
Computational Statistics & Data Analysis
Sparse estimation in functional linear regression
Journal of Multivariate Analysis
Regression when both response and predictor are functions
Journal of Multivariate Analysis
| Hi-index | 0.03 |
Scalar-on-function regression problems with continuous outcomes arise naturally in many settings, and a wealth of estimation methods now exist. Despite the clear differences in regression model assumptions, tuning parameter selection, and the incorporation of functional structure, it remains common to apply a single method to any dataset of interest. In this paper we develop tools for estimator selection and combination in the context of continuous scalar-on-function regression based on minimizing the cross-validated prediction error of the final estimator. A broad collection of functional and high-dimensional regression methods is used as a library of candidate estimators. We find that the performance of any single method relative to others can vary dramatically across datasets, but that the proposed cross-validation procedure is consistently among the top performers. Four real-data analyses using publicly available benchmark datasets are presented; code implementing these analyses and facilitating the application of proposed methods on future datasets is available in a web supplement.