Linearity Identification for General Partial Linear Single-Index Models
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-09-27
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
Partial linear models, a family of popular semiparametric models, provide us with an interpretable and flexible assumption for modelling complex data.
One challenging question in partial linear models is the structure identification for the linear components and the nonlinear components, especially for high dimensional data.
This paper considers the structure identification problem in the general partial linear single-index models, where the link function is unknown.
We propose two penalized methods based on a modern dimension reduction technique.
Under certain regularity conditions, we show that the second estimator is able to identify the underlying true model structure correctly.
The convergence rate of the new estimator is established as well.
American Psychological Association (APA)
Lv, Shaogao& Wang, Luhong. 2016. Linearity Identification for General Partial Linear Single-Index Models. Mathematical Problems in Engineering،Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1112037
Modern Language Association (MLA)
Lv, Shaogao& Wang, Luhong. Linearity Identification for General Partial Linear Single-Index Models. Mathematical Problems in Engineering No. 2016 (2016), pp.1-7.
https://search.emarefa.net/detail/BIM-1112037
American Medical Association (AMA)
Lv, Shaogao& Wang, Luhong. Linearity Identification for General Partial Linear Single-Index Models. Mathematical Problems in Engineering. 2016. Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1112037
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1112037