Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10328129 | Computational Statistics & Data Analysis | 2005 | 14 Pages |
Abstract
A new method for the estimation of smooth monotone regression functions is proposed. It is assumed that the monotonicity may come from some physical or economic reason. A monotone estimator of an integral (of a positive function) using a gradient boosting method is derived. The proposed method generates a sequence of fits without monotone constraints and combines them to form a monotone estimate. An advantage of the proposed algorithm is that one can use a popular smoothing technique without the monotone constraint as the base learner. The performance of the proposed procedure is demonstrated on both simulated and real data sets.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Yuwon Kim, Ja-Yong Koo,