The simulation extrapolation technique meets ecology and evolution: A general and intuitive method to account for measurement error

Abstract

Measurement error and other forms of uncertainty are commonplace in ecology and evolution, and may bias estimates of parameters of interest. Although a variety of approaches to obtain unbiased estimators are available, these usually require the formulation of an explicit (parametric) model for the error‐prone variable and a latent model for the unobserved (latent) error‐free variable. In practice, this is often difficult. We propose to generalize the simulation extrapolation (SIMEX) technique, a heuristic approach to correct for m Show more