The optimal selection for restricted linear models with average estimator
type: Item
instance of: scholarly article
title: The optimal selection for restricted linear models with average estimator
zbMATH Open document ID: 1474.91189
DOI: 10.1155/2014/692472
author: Qichang Xie
author: Meng Du
published in: Abstract and Applied Analysis
publication date: 2019-02-14T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 91G10
Mathematics Subject Classification ID: 62F10
Mathematics Subject Classification ID: 62J05
zbMATH DE Number: 7022893
Wikidata QID: Q59040667
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/692472
OpenAlex ID: W2126772958
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description: scientific article; zbMATH DE number 7022893