Unbiased generalized quasi-regression

Authors:
Guijun Yang;Zhigang Wang;Wei Deng
Affiliations:
Department of Statistics, Tianjin University of Finance and Economics, China;Department of Statistics, Tianjin University of Finance and Economics, China and Department of Statistics and Mathematics, Inner Mongolia Finance and Economics College, China;Department of Statistics, Tianjin University of Finance and Economics, China
Venue:
Computational Statistics & Data Analysis
Year:
2010

Citing 3
Cited 1

Quasi-regression with shrinkage

Mathematics and Computers in Simulation - Special issue: 3rd IMACS seminar on Monte Carlo methods - MCM 2001
On testing a subset of regression parameters under heteroskedasticity

Computational Statistics & Data Analysis
A graph approach to generate all possible regression submodels

Computational Statistics & Data Analysis

Editorial: Second special issue on statistical algorithms and software

Computational Statistics & Data Analysis

Quantified Score

Hi-index	0.03

Visualization

Abstract

Quasi-regression and generalized quasi-regression have been used as an approximation to an unknown function on the unit cube of very high dimensions. However, the fitting functions constructed by the two methods in the literature have biases. A new method called unbiased generalized quasi-regression is introduced. Theoretical results show that the new estimators of scalar coefficients and the fitting function have unbiasedness at the same time. Several numerical examples demonstrate that the unbiased generalized quasi-regression often has smaller residual errors than quasi-regression and generalized quasi-regression.