Journal
COMPUTATIONAL STATISTICS & DATA ANALYSIS
Volume 70, Issue -, Pages 362-372Publisher
ELSEVIER
DOI: 10.1016/j.csda.2013.10.009
Keywords
Cross validation; Functional linear model; Model stacking; Super learning
Funding
- German Research Foundation (DFG) through the Emmy Noether Program [3739/1-1]
Ask authors/readers for more resources
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. (C) 2013 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available