The Optimal Selection for Restricted Linear Models with Average Estimator
Joint Authors
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-05-21
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
The essential task of risk investment is to select an optimal tracking portfolio among various portfolios.
Statistically, this process can be achieved by choosing an optimal restricted linear model.
This paper develops a statistical procedure to do this, based on selecting appropriate weights for averaging approximately restricted models.
The method of weighted average least squares is adopted to estimate the approximately restricted models under dependent error setting.
The optimal weights are selected by minimizing a k-class generalized information criterion (k-GIC), which is an estimate of the average squared error from the model average fit.
This model selection procedure is shown to be asymptotically optimal in the sense of obtaining the lowest possible average squared error.
Monte Carlo simulations illustrate that the suggested method has comparable efficiency to some alternative model selection techniques.
American Psychological Association (APA)
Xie, Qichang& Du, Meng. 2014. The Optimal Selection for Restricted Linear Models with Average Estimator. Abstract and Applied Analysis،Vol. 2014, no. 2014, pp.1-13.
https://search.emarefa.net/detail/BIM-1014595
Modern Language Association (MLA)
Xie, Qichang& Du, Meng. The Optimal Selection for Restricted Linear Models with Average Estimator. Abstract and Applied Analysis No. 2014 (2014), pp.1-13.
https://search.emarefa.net/detail/BIM-1014595
American Medical Association (AMA)
Xie, Qichang& Du, Meng. The Optimal Selection for Restricted Linear Models with Average Estimator. Abstract and Applied Analysis. 2014. Vol. 2014, no. 2014, pp.1-13.
https://search.emarefa.net/detail/BIM-1014595
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1014595