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Efficient algorithms for computing the best subset regression models for large-scale problems

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  • Hofmann, Marc
  • Gatu, Cristian
  • Kontoghiorghes, Erricos John

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  • 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.
    • Handle: RePEc:eee:csdana:v:52:y:2007:i:1:p:16-29
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    References listed on IDEAS

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    1. Cristian Gatu & Erricos Kontoghiorghes, 2005. "Efficient strategies for deriving the subset VAR models," Computational Management Science, Springer, vol. 4(4), pages 253-278, November.
    2. Cristian Gatu & Erricos Kontoghiorghes, 2002. "A branch and bound algorithm for computing the best subset regression models," Computing in Economics and Finance 2002 294, Society for Computational Economics.
    3. 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.
    4. M. R. B. Clarke, 1981. "A Givens Algorithm for Moving from One Linear Model to Another Without Going Back to the Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 30(2), pages 198-203, June.
    5. Sarah J. Roberts, 1984. "A Branch and Bound Algorithm for Determining the Optimal Feature Subset of Given Size," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 33(2), pages 236-241, June.
    6. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    7. Smith, D. M. & Bremner, J. M., 1989. "All possible subset regressions using the QR decomposition," Computational Statistics & Data Analysis, Elsevier, vol. 7(3), pages 217-235, February.
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    Cited by:

    1. 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.
    2. Fossati, Sebastian, 2012. "Covariate unit root tests with good size and power," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3070-3079.
    3. Buchholz, Anika & Hollander, Norbert & Sauerbrei, Willi, 2008. "On properties of predictors derived with a two-step bootstrap model averaging approach--A simulation study in the linear regression model," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2778-2793, January.
    4. 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.
    5. Postiglione, Paolo & Benedetti, Roberto & Lafratta, Giovanni, 2010. "A regression tree algorithm for the identification of convergence clubs," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2776-2785, November.
    6. Thompson, Ryan, 2022. "Robust subset selection," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
    7. Pacheco, Joaquín & Casado, Silvia & Porras, Santiago, 2013. "Exact methods for variable selection in principal component analysis: Guide functions and pre-selection," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 95-111.
    8. Ouysse, Rachida & Kohn, Robert, 2010. "Bayesian variable selection and model averaging in the arbitrage pricing theory model," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3249-3268, December.
    9. Shafik, Nivien & Tutz, Gerhard, 2009. "Boosting nonlinear additive autoregressive time series," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2453-2464, May.
    10. Brusco, Michael J., 2014. "A comparison of simulated annealing algorithms for variable selection in principal component analysis and discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 38-53.
    11. Miyashiro, Ryuhei & Takano, Yuichi, 2015. "Mixed integer second-order cone programming formulations for variable selection in linear regression," European Journal of Operational Research, Elsevier, vol. 247(3), pages 721-731.
    12. Yang, Guijun & Wang, Zhigang & Deng, Wei, 2010. "Unbiased generalized quasi-regression," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 779-789, March.
    13. Paroli, Roberta & Spezia, Luigi, 2008. "Bayesian inference in non-homogeneous Markov mixtures of periodic autoregressions with state-dependent exogenous variables," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2311-2330, January.
    14. Salibian-Barrera, Matias & Van Aelst, Stefan, 2008. "Robust model selection using fast and robust bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5121-5135, August.
    15. Siniksaran, Enis, 2008. "A geometric interpretation of Mallows' Cp statistic and an alternative plot in variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3459-3467, March.
    16. Gilli, Manfred & Winker, Peter, 2007. "2nd Special Issue on Applications of Optimization Heuristics to Estimation and Modelling Problems," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 2-3, September.
    17. Khan, Jafar A. & Van Aelst, Stefan & Zamar, Ruben H., 2010. "Fast robust estimation of prediction error based on resampling," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3121-3130, December.
    18. Gatu, Cristian & Kontoghiorghes, Erricos J. & Gilli, Manfred & Winker, Peter, 2008. "An efficient branch-and-bound strategy for subset vector autoregressive model selection," Journal of Economic Dynamics and Control, Elsevier, vol. 32(6), pages 1949-1963, June.
    19. Guo, Yi & Berman, Mark & Gao, Junbin, 2014. "Group subset selection for linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 39-52.

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