Sparse Data, Estimational Reliability, and Risk‐Efficient Decisions

Abstract

Abstract The minimal data required for a reasonable estimation of probability distributions is investigated through a Monte Carlo study of a rule for smoothing sparse data into cumulative distribution functions. In a set of estimated distributions, risky prospects not preferred by risk‐averse decision makers can be identified and discarded.