On the Degrees of Freedom of Mixed Matrix Regression
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-09-18
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
With the increasing prominence of big data in modern science, data of interest are more complex and stochastic.
To deal with the complex matrix and vector data, this paper focuses on the mixed matrix regression model.
We mainly establish the degrees of freedom of the underlying stochastic model, which is one of the important topics to construct adaptive selection criteria for efficiently selecting the optimal model fit.
Under some mild conditions, we prove that the degrees of freedom of mixed matrix regression model are the sum of the degrees of freedom of Lasso and regularized matrix regression.
Moreover, we establish the degrees of freedom of nuclear-norm regularization multivariate regression.
Furthermore, we prove that the estimates of the degrees of freedom of the underlying models process the consistent property.
American Psychological Association (APA)
Shang, Pan& Kong, Lingchen. 2017. On the Degrees of Freedom of Mixed Matrix Regression. Mathematical Problems in Engineering،Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1191601
Modern Language Association (MLA)
Shang, Pan& Kong, Lingchen. On the Degrees of Freedom of Mixed Matrix Regression. Mathematical Problems in Engineering No. 2017 (2017), pp.1-8.
https://search.emarefa.net/detail/BIM-1191601
American Medical Association (AMA)
Shang, Pan& Kong, Lingchen. On the Degrees of Freedom of Mixed Matrix Regression. Mathematical Problems in Engineering. 2017. Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1191601
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1191601