New algorithms for statistical analysis of interval data (2004)
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| Venue: | Proceedings of the Workshop on State-of-the-Art in Scientific Computing PARA’04 |
| Citations: | 12 - 9 self |
BibTeX
@INPROCEEDINGS{Xiang04newalgorithms,
author = {Gang Xiang and Scott A. Starks and Vladik Kreinovich and Luc Longpré},
title = {New algorithms for statistical analysis of interval data},
booktitle = {Proceedings of the Workshop on State-of-the-Art in Scientific Computing PARA’04},
year = {2004},
pages = {123--129}
}
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Abstract
Abstract. 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. 1
Citations
| 169 | Computational complexity and feasibility of data processing and interval computations - Kreinovich, Lakeyev, et al. - 1998 |
| 56 | Computing Variance for Interval Data is NPHard - Ferson, Ginzburg, et al. |
| 27 | Novel Approaches to Numerical Software with Result Verification - Granvilliers, Kreinovich, et al. - 2004 |
| 7 | Stein.Introduction to Algorithms - Cormen, Leiserson, et al. - 2001 |
