The discarding of variables in multivariate analysis.

By E. Beale, M. Kendall, D. W. Mann. 1967

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Citations in the corpus

Extended Comparisons of Best Subset Selection, Forward Stepwise Selection, and the Lasso (2017, neutral): “The first two are classical methods in statistics, dating back to at least Beale et al. (1967); Hocking and Leslie (1967) for best subset selection and Efroymson (1966); Draper and Smith (1966) for forward selection; the lasso is (relatively speaking) more recent, due to Tibshirani (1996); Chen et a”
Ridge Regression: Biased Estimation for Nonorthogonal Problems (2000, methodology): “These automatic procedures include regression using the factors obtained from a coordinate transformation using the principal components of X'X, stepwise regression, computation of all 2P regressions, and some subset of all regressions using fractional factorials or a branch and bound technique [3][”
Interactions between climate and habitat loss effects on biodiversity: a systematic review and meta‐analysis (2012, methodology): “We used a step-wise forward selection analysis to test the significance of random effects before including them in the logistic-regression analyses (Beale et al. 1967).”
Robust stepwise regression (2002, methodology): “In the 1960s, a method was developed called Stepwise regression (Efroymson, 1960; Goldberger & Jochems, 1961; Goldberger, 1961), which chose a subset of explanatory variables exploring only a few possible sub-models (Garside, 1965; Beale et al., 1967).”
Algorithm for cardinality-constrained quadratic optimization (2009, background): “Authors of [2, 9, 10] use pivoting and enumeration to search for subsets with the best regression “fit” (e.”