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題名 On-line algorithms for computing mean and variance of interval data, and their use in intelligent systems
作者 Kreinovich, V.
吳柏林
Wu, Berlin
Nguyen, H.T.
貢獻者 應數系
關鍵詞 Algorithms; Data processing; Data reduction; Intelligent systems; Online systems; Statistical process control; Interval data; Mean; On-line data processing; Variance; Computation theory
日期 2007-08
上傳時間 13-Jul-2015 17:11:26 (UTC+8)
摘要 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.
關聯 Information Sciences, 177(16), 3228-3238
資料類型 article
DOI http://dx.doi.org/10.1016/j.ins.2006.11.007
dc.contributor 應數系-
dc.creator (作者) Kreinovich, V.-
dc.creator (作者) 吳柏林zh_TW
dc.creator (作者) Wu, Berlinen_US
dc.creator (作者) Nguyen, H.T.en_US
dc.date (日期) 2007-08-
dc.date.accessioned 13-Jul-2015 17:11:26 (UTC+8)-
dc.date.available 13-Jul-2015 17:11:26 (UTC+8)-
dc.date.issued (上傳時間) 13-Jul-2015 17:11:26 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/76547-
dc.description.abstract (摘要) 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.-
dc.format.extent 189513 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) Information Sciences, 177(16), 3228-3238-
dc.subject (關鍵詞) Algorithms; Data processing; Data reduction; Intelligent systems; Online systems; Statistical process control; Interval data; Mean; On-line data processing; Variance; Computation theory-
dc.title (題名) On-line algorithms for computing mean and variance of interval data, and their use in intelligent systems-
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.1016/j.ins.2006.11.007-
dc.doi.uri (DOI) http://dx.doi.org/10.1016/j.ins.2006.11.007-