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| Field |
Value |
| 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: | http://dx.doi.org/10.1016/j.ins.2006.11.007 |
| DCField |
Value |
Language |
| dc.contributor (Contributor) | 應數系 | - |
| dc.creator (Authors) | Kreinovich, V. | - |
| dc.creator (Authors) | 吳柏林 | zh_TW |
| dc.creator (Authors) | Wu, Berlin | en_US |
| dc.creator (Authors) | Nguyen, H.T. | en_US |
| dc.date (Date) | 2007-08 | - |
| dc.date.accessioned | 2015-07-13 17:11:26 (UTC+8) | - |
| dc.date.available | 2015-07-13 17:11:26 (UTC+8) | - |
| dc.date.issued (Issue Date) | 2015-07-13 17:11:26 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/76547 | - |
| dc.description.abstract (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. | - |
| dc.format.extent | 189513 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (Relation) | Information Sciences, 177(16), 3228-3238 | - |
| dc.subject (Keywords) | Algorithms; Data processing; Data reduction; Intelligent systems; Online systems; Statistical process control; Interval data; Mean; On-line data processing; Variance; Computation theory | - |
| dc.title (Title) | On-line algorithms for computing mean and variance of interval data, and their use in intelligent systems | - |
| dc.type (Data Type) | article | en |
| dc.identifier.doi (DOI) | 10.1016/j.ins.2006.11.007 | - |
| dc.doi.uri | http://dx.doi.org/10.1016/j.ins.2006.11.007 | - |
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