The convergence rate of the hazard function with functional explanatory variable: case of spacial data with k Nearest Neighbor method
Main Article Content
Abstract
In this paper we introduced a new hazard estimator when the co-variables are functional in nature. This estimator is a mix of both the k Nearest Neighbors shortly (kNN) procedure and spacial functional data. Then the convergence rate are introduced when the considered sample is collected in spatial order with mixing structure. In theory there is an estimation of the risk point then a discussion of application difficulties, such as data driven bandwidth choice. Furthermore, a comparison study based on simulated and real data is also provided to illustrate the performances and the usefulness of the kNN approach and to prove the highly sensitive of the kNN approach to the presence of even a small proportion of outliers in the data
Downloads
Metrics
Article Details
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
