Asymptotic algorithm for computing the sample variance of interval data

The problem of the sample variance computation for epistemic interval-valued data is, in general, NP-hard. Therefore, known ecient algorithms for computing variance require strong restrictions on admissible intervals like the no-subset property or heavy limitations on the number of possible intersections between intervals. A new asymptotic algorithm for computing the upper bound of the sample variance in a feasible time is proposed. Conditions required for its application with nite samples are discussed and some properties of the algorithm are also given. It appears that our new algorithm could be effectively applied in denitely more situations than methods used so far.