Publication selection bias, i.e. the selective reporting of statistically significant results, is a serious problem for any empirical research. As consequence, the estimated effects in the literature can appear considerably larger/smaller than they are and more specifically meta-estimated effect size can be severely biased. Recently, several methods of publication correction have been proposed. None of these methods, however, works reasonably well in general. In particular, PET performs well if the true eﬀect is zero, PEESE only works well for large true eﬀects, and Top 10 also works well for large true, but is ad-hoc and ineﬃcient. We propose a novel publication bias method that works well across a wide range of eﬀect sizes, publication bias incidences, and meta-sample sizes. It also outperforms a combination of PET and PEESE in a variety of scenarios. Extensive simulations illustrate the gains in terms of efficiency as well as bias.