Posterior asymptotic normality for an individual coordinate in high-dimensional linear regression. (English) Zbl 1429.62306

Summary: It is well known that high-dimensional procedures like the Lasso provide biased estimators of parameters in a linear model. In [J. R. Stat. Soc., Ser. B, Stat. Methodol. 76, No. 1, 217–242 (2014; Zbl 1411.62196)], C.-H. Zhang and S. S. 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.


62J05 Linear regression; mixed models
62J07 Ridge regression; shrinkage estimators (Lasso)


Zbl 1411.62196
Full Text: DOI arXiv Euclid


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