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The Best Sub‐Set in Multiple Regression Analysis

Author

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  • M. J. Garside

Abstract

In this article, Mr Garside gives a procedure for comparing all sub‐sets in multiple regression analysis and thereby obtaining the best sub‐set of a given size in the sense of the minimum residual sum of squares. The author also points out that this is a special case of a more general problem.

Suggested Citation

  • M. J. Garside, 1965. "The Best Sub‐Set in Multiple Regression Analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 14(2-3), pages 196-200, November.
    • Handle: RePEc:bla:jorssc:v:14:y:1965:i:2-3:p:196-200
    DOI: 10.2307/2985341
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    Cited by:

    1. Rafael Blanquero & Emilio Carrizosa & Pepa Ramírez-Cobo & M. Remedios Sillero-Denamiel, 2021. "A cost-sensitive constrained Lasso," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(1), pages 121-158, March.
    2. Yoshiki Ogawa & Yoshihide Sekimoto & Ryosuke Shibasaki, 2021. "Estimation of earthquake damage to urban environments using sparse modeling," Environment and Planning B, , vol. 48(5), pages 1075-1090, June.
    3. Liu, Honghu & Weiss, Robert E. & Jennrich, Robert I. & Wenger, Neil S., 1999. "PRESS model selection in repeated measures data," Computational Statistics & Data Analysis, Elsevier, vol. 30(2), pages 169-184, April.
    4. Thompson, Ryan, 2022. "Robust subset selection," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
    5. Andrés Gómez & Oleg A. Prokopyev, 2021. "A Mixed-Integer Fractional Optimization Approach to Best Subset Selection," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 551-565, May.

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