It is well known that high-dimensional procedures like the LASSO provide biased estimators of parameters in a linear model. In a 2014 paper Zhang and Zhang showed how to remove this bias by means of a two-step procedure. We show that de-biasing can also be achieved by a one-step estimator, the form of which inspires the development of a Bayesian analogue of the frequentists’ de-biasing techniques.
"Posterior asymptotic normality for an individual coordinate in high-dimensional linear regression." Electron. J. Statist. 13 (2) 3082 - 3094, 2019. https://doi.org/10.1214/19-EJS1605