Comparison of directional random walk and weighted least squares modeling of sparse fossil data
This paper demonstrates through simulations and weighted mean squared error comparisons that weighted least squares (WLS) is a superior and more flexible method than the general random walk (GRW) model for analyzing sparse fossil data with realistic measurement errors, as GRW struggles to estimate parameters accurately and may yield overly optimistic AIC results under such conditions.