Open Access
2014 The Optimal Selection for Restricted Linear Models with Average Estimator
Qichang Xie, Meng Du
Abstr. Appl. Anal. 2014: 1-13 (2014). DOI: 10.1155/2014/692472


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.


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Qichang Xie. Meng Du. "The Optimal Selection for Restricted Linear Models with Average Estimator." Abstr. Appl. Anal. 2014 1 - 13, 2014.


Published: 2014
First available in Project Euclid: 2 October 2014

zbMATH: 07022893
MathSciNet: MR3214447
Digital Object Identifier: 10.1155/2014/692472

Rights: Copyright © 2014 Hindawi

Vol.2014 • 2014
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