Appropriate and Inappropriate Estimation Techniques
This work clarifies foundational statistical estimation techniques for researchers in Bayesian inference, though it appears incremental as it formalizes known relationships.
The paper examines when mode, mean, and median estimation yield cost-minimizing estimates under different cost functions, finding that mode estimation works only for 0-1 cost, mean for squared distance, and median for absolute distance.
Mode {also called MAP} estimation, mean estimation and median estimation are examined here to determine when they can be safely used to derive {posterior) cost minimizing estimates. (These are all Bayes procedures, using the mode. mean. or median of the posterior distribution). It is found that modal estimation only returns cost minimizing estimates when the cost function is 0-t. If the cost function is a function of distance then mean estimation only returns cost minimizing estimates when the cost function is squared distance from the true value and median estimation only returns cost minimizing estimates when the cost function ts the distance from the true value. Results are presented on the goodness or modal estimation with non 0-t cost functions