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