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Remodeling and Estimation for Sparse Partially Linear Regression Models
Joint Authors
Lin, Lu
Wang, Xiuli
Zeng, Yunhui
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-02-06
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
When the dimension of covariates in the regression model is high, one usually uses a submodel as a working model that contains significant variables.
But it may be highly biased and the resulting estimator of the parameter of interest may be very poor when the coefficients of removed variables are not exactly zero.
In this paper, based on the selected submodel, we introduce a two-stage remodeling method to get the consistent estimator for the parameter of interest.
More precisely, in the first stage, by a multistep adjustment, we reconstruct an unbiased model based on the correlation information between the covariates; in the second stage, we further reduce the adjusted model by a semiparametric variable selection method and get a new estimator of the parameter of interest simultaneously.
Its convergence rate and asymptotic normality are also obtained.
The simulation results further illustrate that the new estimator outperforms those obtained by the submodel and the full model in the sense of mean square errors of point estimation and mean square prediction errors of model prediction.
American Psychological Association (APA)
Zeng, Yunhui& Wang, Xiuli& Lin, Lu. 2013. Remodeling and Estimation for Sparse Partially Linear Regression Models. Abstract and Applied Analysis،Vol. 2013, no. 2013, pp.1-11.
https://search.emarefa.net/detail/BIM-490609
Modern Language Association (MLA)
Zeng, Yunhui…[et al.]. Remodeling and Estimation for Sparse Partially Linear Regression Models. Abstract and Applied Analysis No. 2013 (2013), pp.1-11.
https://search.emarefa.net/detail/BIM-490609
American Medical Association (AMA)
Zeng, Yunhui& Wang, Xiuli& Lin, Lu. Remodeling and Estimation for Sparse Partially Linear Regression Models. Abstract and Applied Analysis. 2013. Vol. 2013, no. 2013, pp.1-11.
https://search.emarefa.net/detail/BIM-490609
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-490609