Model averaging estimator in ridge regression and its large sample properties View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-04-18

AUTHORS

Shangwei Zhao, Jun Liao, Dalei Yu

ABSTRACT

In linear regression, when the covariates are highly collinear, ridge regression has become the standard treatment. The choice of ridge parameter plays a central role in ridge regression. In this paper, instead of ending up with a single ridge parameter, we consider a model averaging method to combine multiple ridge estimators with Mn different ridge parameters, where Mn can go to infinity with sample size n. We show that when the fitting model is correctly specified, the resulting model averaging estimator is n1/2-consistent. When the fitting model is misspecified, the asymptotic optimality of the model averaging estimator is also established rigorously. The results of simulation studies and our case study concerning the urbanization level of Chinese ethnic areas demonstrate the usefulness of the model averaging method. More... »

PAGES

1-21

References to SciGraph publications

  • 2013-06. Frequentist model averaging for linear mixed-effects models in FRONTIERS OF MATHEMATICS IN CHINA
  • 2011-12. Significance testing in ridge regression for genetic data in BMC BIOINFORMATICS
  • 2009-12. Frequentist model averaging estimation: a review in JOURNAL OF SYSTEMS SCIENCE AND COMPLEXITY
  • 2012-11. Shrinkage averaging estimation in STATISTICAL PAPERS
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    http://scigraph.springernature.com/pub.10.1007/s00362-018-1002-4

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    http://dx.doi.org/10.1007/s00362-018-1002-4

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