Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1144507 | Journal of the Korean Statistical Society | 2016 | 13 Pages |
Abstract
Interval-valued data are observed as ranges instead of single values and frequently appear with advanced technologies in current data collection processes. Regression analysis of interval-valued data has been studied in the literature, but mostly focused on parametric linear regression models. In this paper, we study interval-valued data regression based on nonparametric additive models. By employing one of the current methods based on linear regression, we propose a nonparametric additive approach to properly analyze interval-valued data with a possibly nonlinear pattern. We demonstrate the proposed approach using a simulation study and a real data example, and also compare its performance with those of existing methods.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Changwon Lim,