Open Journal of Statistics

Volume 9, Issue 5 (October 2019)

ISSN Print: 2161-718X   ISSN Online: 2161-7198

Google-based Impact Factor: 0.69  Citations  h5-index & Ranking

Variance Estimation for High-Dimensional Varying Index Coefficient Models

Full-Text HTML  XML Download Download as PDF (Size: 661KB)  PP. 555-570  
DOI: 10.4236/ojs.2019.95037    217 Downloads   457 Views  
Author(s)

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.

Cite this paper

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.

Cited by

No relevant information.

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.