Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/76547


Title: On-line algorithms for computing mean and variance of interval data, and their use in intelligent systems
Authors: Kreinovich, V.
吳柏林
Wu, Berlin
Nguyen, H.T.
Contributors: 應數系
Keywords: Algorithms;Data processing;Data reduction;Intelligent systems;Online systems;Statistical process control;Interval data;Mean;On-line data processing;Variance;Computation theory
Date: 2007-08
Issue Date: 2015-07-13 17:11:26 (UTC+8)
Abstract: When we have only interval ranges [under(x, {combining low line})i, over(xi, -)] of sample values x1, ..., xn, what is the interval [under(V, {combining low line}), over(V, -)] of possible values for the variance V of these values? There are quadratic time algorithms for computing the exact lower bound V on the variance of interval data, and for computing over(V, -) under reasonable easily verifiable conditions. The problem is that in real life, we often make additional measurements. In traditional statistics, if we have a new measurement result, we can modify the value of variance in constant time. In contrast, previously known algorithms for processing interval data required that, once a new data point is added, we start from the very beginning. In this paper, we describe new algorithms for statistical processing of interval data, algorithms in which adding a data point requires only O(n) computational steps. © 2006 Elsevier Inc. All rights reserved.
Relation: Information Sciences, 177(16), 3228-3238
Data Type: article
DOI 連結: http://dx.doi.org/10.1016/j.ins.2006.11.007
Appears in Collections:[應用數學系] 期刊論文

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