New Algorithms for Statistical Analysis of Interval Data

It is known that in general, statistical analysis of interval data is an NP-hard problem: even computing the variance of interval data is, in general, NP-hard. Until now, only one case was known for which a feasible algorithm can compute the variance of interval data: the case when all the measurements are accurate enough – so that even after the measurement, we can distinguish between different measured values xi. In this paper, we describe several new cases in which feasible algorithms are possible – e.g., the case when all the measurements are done by using the same (not necessarily very accurate) measurement instrument – or at least a limited number of different measuring instruments.

• Type: Conference
• Year: 2004
• Where: PARA
• Authors: Gang Xiang, Scott A. Starks, Vladik Kreinovich, Luc Longpré
• Keywords: Applied Computing, Feasible Algorithms, Interval Data, NP-hard Problem, PARA 2004