This paper develops a bias correction scheme for reparametrized inverse gamma regression models with varying precision [Bourguignon M, Gallardo DI. Reparametrized precision. Stat Neerl. 2020;74(4):611–627], which is tailored to situations where the response variable has an asymmetrical shape on positive real line. In particular, we discuss maximum-likelihood estimation model parameters and deri...

Observed data are often contaminated by undiscovered interlopers, leading to biased parameter estimation. Here we present BEAMS (Bayesian estimation applied to multiple species) which significantly improves on the standard maximum likelihood approach in the case where the probability for each data point being ‘‘pure’’ is known. We discuss the application of BEAMS to future type-Ia supernovae (S...