Unbiased generalized quasi-regression
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- 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.
- Hofmann, Marc & Gatu, Cristian & Kontoghiorghes, Erricos John, 2007. "Efficient algorithms for computing the best subset regression models for large-scale problems," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 16-29, September.
- Wen, Miin-Jye & Chen, Shun-Yi & Chen, Hubert J., 2007. "On testing a subset of regression parameters under heteroskedasticity," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5958-5976, August.
- 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.
- 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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