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Unbiased generalized quasi-regression

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  • Yang, Guijun
  • Wang, Zhigang
  • Deng, Wei

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.

Suggested Citation

  • Yang, Guijun & Wang, Zhigang & Deng, Wei, 2010. "Unbiased generalized quasi-regression," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 779-789, March.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:3:p:779-789
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    References listed on IDEAS

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    1. Gatu, Cristian & Yanev, Petko I. & Kontoghiorghes, Erricos J., 2007. "A graph approach to generate all possible regression submodels," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 799-815, October.
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    4. Kapetanios, George, 2007. "Variable selection in regression models using nonstandard optimisation of information criteria," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 4-15, September.
    5. Jiang, Tao & Owen, Art B., 2003. "Quasi-regression with shrinkage," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 62(3), pages 231-241.
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