Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/120062


Title: Interval-valued and fuzzy-valued random variables: from computing sample variances to computing sample covariances
Authors: Beck, Jan B.
Kreinovich, Vladi
吳柏林
Wu, Berlin
Contributors: 應數系
Keywords: Interval Arithmetic;Interval Uncertainty;Random Interval;Interval Computation;Computing Versus
Date: 2004
Issue Date: 2018-09-11 17:58:53 (UTC+8)
Abstract: Due to measurement uncertainty, often, instead of the actual values xi of the measured quantities, we only know the intervals xi=[x~i−Δi,x~i+Δi], where x~i is the measured value and Δi is the upper bound on the measurement error (provided, e.g., by the manufacturer of the measuring instrument). These intervals can be viewed as random intervals, i.e., as samples from the interval-valued random variable. In such situations, instead of the exact value of the sample statistics such as the covariance Cxy, we can only have an interval Cx,y of possible values of this statistic. It is known that in general, computing such an interval Cx,y for Cxy is an NP-hard problem. In this paper, we describe an algorithm that computes this range Cx,y for the case when the measurements are accurate enough—so that the intervals corresponding to different measurements do not intersect much.
Relation: Soft methodology and random information systems, 85-92, Adv. Soft Comput., Springer, Berlin, 2004
Part of the Advances in Soft Computing book series (AINSC, volume 26)
Data Type: conference
DOI 連結: https://doi.org/10.1007/978-3-540-44465-7_9
Appears in Collections:[應用數學系] 會議論文

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