Performance of some boundary-seeking mode estimators on the dome bias model

Expert estimates can be systematically biased for various reasons. The dome perspective bias model provides one instance of this phenomenon. Given data with this suspected property, it is desirable to propose mode estimators which have the capability of producing consensus estimates on the boundary of the convex hull of the sample. Affine linear models are no doubt the simplest class of functions with that capability. This paper uses the maximum decisional efficiency (MDE) principle to estimate the parameters of an affine linear group value function. These estimators vary according to the sample aggregator chosen. Estimators are developed or approximated for the aggregator choices of (i) mean, (ii) minimum or Leontief, and (iii) variance. The respective performances of these estimators are assessed and compared on the dome perspective bias model using Monte Carlo simulation experiments. The estimator based on the mean performed uniformly well on a variety of simulated cases. However, those based on range and variance were not effective.