Regressions by Leaps and Bounds


Furnival, George M.; Wilson, Robert W., Jr.   (2000, ASQ and American Statistical Association)   Yale University, New Haven, CT; USDA Forest Service

Technometrics    Vol. 42    No. 1
QICID: 13868    February 2000    pp. 69-79
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Article Abstract

[This abstract is based on the authors' abstract.] Several algorithms are described for computing the residual sums of squares for all possible regressions with what appears to be a minimum of arithmetic (less than six floating-point operations per regression.) It is shown how two of these algorithms can be combined to form a simple leap and bound technique for finding the best subsets without examining all possible subsets. The result is a reduction of several orders of magnitude in the number of operations required to find the best subsets.


Linear regression,Statistics

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