When Does the First Spurious Variable Get Selected by Sequential Regression Procedures?
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Applied statisticians use sequential regression procedures to produce a ranking of explanatory variables and, in settings of low correlations between variables and strong true effect sizes, expect that variables at the very top of this ranking are true. In a regime of certain sparsity levels, however, three examples of sequential procedures---forward stepwise, the lasso, and least angle regression---are shown to include the first spurious variable unexpectedly early. We derive a rigorous, sharp prediction of the rank of the first spurious variable for the three procedures, demonstrating that the first spurious variable occurs earlier and earlier as the regression coefficients get denser. This counterintuitive phenomenon persists for independent Gaussian random designs and an arbitrarily large magnitude of the true effects. We further gain a better understanding of the phenomenon by identifying the underlying cause and then leverage the insights to introduce a simple visualization tool termed the "double-ranking diagram" to improve on sequential methods. As a byproduct of these findings, we obtain the first provable result certifying the exact equivalence between the lasso and least angle regression in the early stages of solution paths beyond orthogonal designs. This equivalence can seamlessly carry over many important model selection results concerning the lasso to least angle regression.
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