An Ordered Weights of Evidence Model for Ordered Discrete Variables

摘要

The standard Weights of Evidence (WE) model produces probability estimates for the presence of binary events. However, in many empirical studies the discrete event of interest can take on ordered values. For instance, the presence of mineral deposits may be classified further into different grades. In this paper, a new Ordered Weights of Evidence (OWE) model will be developed. Borrowing the conceptual framework of the latent variable interpretation of the standard Ordered Logistic Regression (OLR) model, the OWE can produce probability estimates for the presence of ordered discrete events. It will be shown that the OWE is computationally less intensive than the OLR. Through a simulation study, it will be shown that the OWE is comparable to the OLR both in terms of in-sample fit and out-of-sample forecasts.