Efficient Approximate Updates of Debiased Lasso Coefficients with Applications to Resampling-Based Variable Selection

The author proposes an approximate formula for updating debiased Lasso coefficients when the design matrix is locally updated, and shows that the approximation error vanishes asymptotically for most coordinates under general non-Gaussian correlated design settings. This allows for efficient implementation of resampling-based variable selection algorithms.