Variance Estimation for High-Dimensional Varying Index Coefficient Models - Open Journal of Statistics

OJS > Vol.9 No.5, October 2019

Variance Estimation for High-Dimensional Varying Index Coefficient Models ()

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Download as PDF (Size: 661KB) PP. 555-570

DOI: 10.4236/ojs.2019.95037 565 Downloads 1,222 Views

Author(s)

Miao Wang^*, Hao Lv, Yicun Wang

Affiliation(s)

Department of Statistics, School of Economics, Jinan University, Guangzhou, China.

ABSTRACT

This paper studies the re-adjusted cross-validation method and a semiparametric regression model called the varying index coefficient model. We use the profile spline modal estimator method to estimate the coefficients of the parameter part of the Varying Index Coefficient Model (VICM), while the unknown function part uses the B-spline to expand. Moreover, we combine the above two estimation methods under the assumption of high-dimensional data. The results of data simulation and empirical analysis show that for the varying index coefficient model, the re-adjusted cross-validation method is better in terms of accuracy and stability than traditional methods based on ordinary least squares.

KEYWORDS

High-Dimensional Data, Refitted Cross-Validation, Varying Index Coefficient Models, Variance Estimation

Share and Cite:

Wang, M. , Lv, H. and Wang, Y. (2019) Variance Estimation for High-Dimensional Varying Index Coefficient Models. Open Journal of Statistics, 9, 555-570. doi: 10.4236/ojs.2019.95037.

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