|
Abstract:
|
When separate populations exhibit similar reliability as a function of multiple explanatory variables, combining them into a single population is tempting. This can simplify future predictions and reduce uncertainty associated with estimation. However, combining these populations may introduce bias if the underlying relationships are in fact different. The probability of agreement formally and intuitively quantifies the similarity of estimated reliability surfaces. In this talk we discuss frequentist and Bayesian approaches to the probability of agreement, and we illustrate the methodology with an example that considers the reliability of single-use munitions.
|
